Overview

Dataset statistics

Number of variables43
Number of observations1573
Missing cells2749
Missing cells (%)4.1%
Duplicate rows34
Duplicate rows (%)2.2%
Total size in memory528.6 KiB
Average record size in memory344.1 B

Variable types

Categorical27
Numeric16

Warnings

currency_buyer has constant value "EUR" Constant
theme has constant value "summer" Constant
crawl_month has constant value "2020-08" Constant
Dataset has 34 (2.2%) duplicate rows Duplicates
title_in_french has a high cardinality: 1201 distinct values High cardinality
title_translated has a high cardinality: 1203 distinct values High cardinality
tags has a high cardinality: 1230 distinct values High cardinality
product_color has a high cardinality: 101 distinct values High cardinality
product_variation_size_id has a high cardinality: 106 distinct values High cardinality
merchant_title has a high cardinality: 958 distinct values High cardinality
merchant_name has a high cardinality: 957 distinct values High cardinality
merchant_info_subtitle has a high cardinality: 1058 distinct values High cardinality
merchant_id has a high cardinality: 958 distinct values High cardinality
merchant_profile_picture has a high cardinality: 125 distinct values High cardinality
product_url has a high cardinality: 1341 distinct values High cardinality
product_picture has a high cardinality: 1341 distinct values High cardinality
product_id has a high cardinality: 1341 distinct values High cardinality
rating_count is highly correlated with rating_five_count and 4 other fieldsHigh correlation
rating_five_count is highly correlated with rating_count and 2 other fieldsHigh correlation
rating_four_count is highly correlated with rating_count and 3 other fieldsHigh correlation
rating_three_count is highly correlated with rating_count and 4 other fieldsHigh correlation
rating_two_count is highly correlated with rating_count and 3 other fieldsHigh correlation
rating_one_count is highly correlated with rating_count and 2 other fieldsHigh correlation
origin_country is highly correlated with theme and 3 other fieldsHigh correlation
urgency_text is highly correlated with theme and 3 other fieldsHigh correlation
theme is highly correlated with origin_country and 12 other fieldsHigh correlation
badge_local_product is highly correlated with theme and 3 other fieldsHigh correlation
badges_count is highly correlated with theme and 3 other fieldsHigh correlation
merchant_has_profile_picture is highly correlated with theme and 3 other fieldsHigh correlation
currency_buyer is highly correlated with origin_country and 12 other fieldsHigh correlation
badge_product_quality is highly correlated with theme and 3 other fieldsHigh correlation
shipping_is_express is highly correlated with theme and 4 other fieldsHigh correlation
crawl_month is highly correlated with origin_country and 12 other fieldsHigh correlation
uses_ad_boosts is highly correlated with theme and 3 other fieldsHigh correlation
has_urgency_banner is highly correlated with origin_country and 12 other fieldsHigh correlation
badge_fast_shipping is highly correlated with theme and 3 other fieldsHigh correlation
shipping_option_name is highly correlated with theme and 4 other fieldsHigh correlation
rating_five_count has 45 (2.9%) missing values Missing
rating_four_count has 45 (2.9%) missing values Missing
rating_three_count has 45 (2.9%) missing values Missing
rating_two_count has 45 (2.9%) missing values Missing
rating_one_count has 45 (2.9%) missing values Missing
product_color has 41 (2.6%) missing values Missing
urgency_text has 1100 (69.9%) missing values Missing
origin_country has 17 (1.1%) missing values Missing
merchant_profile_picture has 1347 (85.6%) missing values Missing
tags is uniformly distributed Uniform
merchant_info_subtitle is uniformly distributed Uniform
product_url is uniformly distributed Uniform
product_picture is uniformly distributed Uniform
product_id is uniformly distributed Uniform
rating_count has 45 (2.9%) zeros Zeros
rating_five_count has 31 (2.0%) zeros Zeros
rating_four_count has 96 (6.1%) zeros Zeros
rating_three_count has 138 (8.8%) zeros Zeros
rating_two_count has 196 (12.5%) zeros Zeros
rating_one_count has 116 (7.4%) zeros Zeros

Reproduction

Analysis started2021-06-18 03:29:54.931348
Analysis finished2021-06-18 03:30:41.811346
Duration46.88 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

title_in_french
Categorical

HIGH CARDINALITY

Distinct1201
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
Nouvelle mode d'été femmes robe décontractée col rond lâche Big Swing jupe sans manches Soild couleur robe de plage
 
24
Mini robe de soirée décontractée sans manches pour femmes
 
12
Femmes d'été Sling Dress V-cou Floral Strap plissé Casual Pocket Large Dress
 
9
Pantalon à lacets à la mode pour femmes d'été, plus la taille Pantalon court à taille haute décontracté
 
9
Tissu taille formateur gilet chaud shaper été shaperwear minceur réglable sueur ceinture corps shaper
 
9
Other values (1196)
1510 

Length

Max length327
Median length112
Mean length116.9211697
Min length27

Characters and Unicode

Total characters183917
Distinct characters99
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique988 ?
Unique (%)62.8%

Sample

1st row2020 Summer Vintage Flamingo Print Pajamas Set Casual Loose T Shirt Top And Elastic Shorts Women Sleepwear Night Wear Loungewear Sets
2nd rowSSHOUSE Summer Casual Sleeveless Soirée Party Soirée sans manches Vêtements de plage sexy Mini robe femme wshC1612242400387A21
3rd row2020 Nouvelle Arrivée Femmes Printemps et Été Plage Porter Longue Mince Cardigan Ouvert Avant Kimono Vert Feuille Imprimé En Mousseline de Soie Cardigan S-5XL
4th rowHot Summer Cool T-shirt pour les femmes Mode Tops Abeille Lettres imprimées Manches courtes O Neck Coton T-shirts Tops Tee Vêtements
5th rowFemmes Shorts d'été à lacets taille élastique lâche mince pantalon décontracté, plus la taille S-8XL
ValueCountFrequency (%)
Nouvelle mode d'été femmes robe décontractée col rond lâche Big Swing jupe sans manches Soild couleur robe de plage24
 
1.5%
Mini robe de soirée décontractée sans manches pour femmes12
 
0.8%
Femmes d'été Sling Dress V-cou Floral Strap plissé Casual Pocket Large Dress9
 
0.6%
Pantalon à lacets à la mode pour femmes d'été, plus la taille Pantalon court à taille haute décontracté9
 
0.6%
Tissu taille formateur gilet chaud shaper été shaperwear minceur réglable sueur ceinture corps shaper9
 
0.6%
Mode féminine été bretelles spaghetti imprimé floral nouer devant mini robe robe sexy8
 
0.5%
Femmes été décontracté lâche couleur unie salopette vintage sangle pantalon long combinaisons barboteuses grande taille7
 
0.4%
Costume de sport cool pour hommes d'été Vêtements de sport Costumes de jogging décontractés Ensembles de tenues à manches courtes7
 
0.4%
Pantalon de mode d'été Femmes Leggings Pantalon déchiré Pantalon slim Pantalon vert armée Collants7
 
0.4%
Mode d'été femme papillon réservoir gilet sans manches col rond haut décontracté6
 
0.4%
Other values (1191)1475
93.8%
2021-06-18T09:00:42.103565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
femmes997
 
3.4%
de916
 
3.1%
robe888
 
3.1%
manches779
 
2.7%
d'été756
 
2.6%
mode644
 
2.2%
taille603
 
2.1%
à585
 
2.0%
sans526
 
1.8%
casual443
 
1.5%
Other values (1937)21976
75.5%

Most occurring characters

ValueCountFrequency (%)
27665
15.0%
e19612
 
10.7%
s11169
 
6.1%
o10437
 
5.7%
a9797
 
5.3%
l8526
 
4.6%
t8444
 
4.6%
r8176
 
4.4%
n8023
 
4.4%
i7652
 
4.2%
Other values (89)64416
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter133795
72.7%
Space Separator27668
 
15.0%
Uppercase Letter18640
 
10.1%
Decimal Number1477
 
0.8%
Other Punctuation1148
 
0.6%
Dash Punctuation1035
 
0.6%
Open Punctuation58
 
< 0.1%
Close Punctuation58
 
< 0.1%
Math Symbol27
 
< 0.1%
Connector Punctuation8
 
< 0.1%
Other values (2)3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e19612
14.7%
s11169
 
8.3%
o10437
 
7.8%
a9797
 
7.3%
l8526
 
6.4%
t8444
 
6.3%
r8176
 
6.1%
n8023
 
6.0%
i7652
 
5.7%
m6949
 
5.2%
Other values (26)35010
26.2%
ValueCountFrequency (%)
S2959
15.9%
C1800
9.7%
T1453
 
7.8%
F1324
 
7.1%
D1317
 
7.1%
P1288
 
6.9%
L1200
 
6.4%
M1199
 
6.4%
B909
 
4.9%
R858
 
4.6%
Other values (18)4333
23.2%
ValueCountFrequency (%)
'915
79.7%
,135
 
11.8%
/39
 
3.4%
"26
 
2.3%
.13
 
1.1%
:8
 
0.7%
&4
 
0.3%
!4
 
0.3%
\2
 
0.2%
%1
 
0.1%
ValueCountFrequency (%)
2341
23.1%
0328
22.2%
5249
16.9%
1175
11.8%
8110
 
7.4%
990
 
6.1%
368
 
4.6%
447
 
3.2%
646
 
3.1%
723
 
1.6%
ValueCountFrequency (%)
(50
86.2%
7
 
12.1%
[1
 
1.7%
ValueCountFrequency (%)
)54
93.1%
3
 
5.2%
]1
 
1.7%
ValueCountFrequency (%)
27665
> 99.9%
 3
 
< 0.1%
ValueCountFrequency (%)
~16
59.3%
+11
40.7%
ValueCountFrequency (%)
-1035
100.0%
ValueCountFrequency (%)
`2
100.0%
ValueCountFrequency (%)
_8
100.0%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin152435
82.9%
Common31482
 
17.1%

Most frequent character per script

ValueCountFrequency (%)
e19612
 
12.9%
s11169
 
7.3%
o10437
 
6.8%
a9797
 
6.4%
l8526
 
5.6%
t8444
 
5.5%
r8176
 
5.4%
n8023
 
5.3%
i7652
 
5.0%
m6949
 
4.6%
Other values (54)53650
35.2%
ValueCountFrequency (%)
27665
87.9%
-1035
 
3.3%
'915
 
2.9%
2341
 
1.1%
0328
 
1.0%
5249
 
0.8%
1175
 
0.6%
,135
 
0.4%
8110
 
0.3%
990
 
0.3%
Other values (25)439
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII178350
97.0%
None5567
 
3.0%

Most frequent character per block

ValueCountFrequency (%)
27665
15.5%
e19612
 
11.0%
s11169
 
6.3%
o10437
 
5.9%
a9797
 
5.5%
l8526
 
4.8%
t8444
 
4.7%
r8176
 
4.6%
n8023
 
4.5%
i7652
 
4.3%
Other values (74)58849
33.0%
ValueCountFrequency (%)
é4126
74.1%
à512
 
9.2%
â307
 
5.5%
É226
 
4.1%
è139
 
2.5%
ê114
 
2.0%
À74
 
1.3%
ô27
 
0.5%
î16
 
0.3%
7
 
0.1%
Other values (5)19
 
0.3%

title_translated
Categorical

HIGH CARDINALITY

Distinct1203
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
New Fashion Summer Women Casual Dress Round Neck Loose Big Swing Skirt Sleeveless Soild Color Beach dress
 
24
Sexy Women's Summer Casual Sleeveless Evening Party Backless Beachwear Mini Dress
 
12
Summer Women s Fashion Lace Up Tie Pants Plus Size Casual High Waist Short Pants
 
9
Fabric Waist Trainer Vest Hot Shaper Summer Shaperwear Slimming Adjustable Sweat Belt Body Shaper
 
9
Summer Women Sling Dress V-neck Floral Pleated Strap Casual Pocket Large Dress
 
9
Other values (1198)
1510 

Length

Max length272
Median length96
Mean length102.6280992
Min length21

Characters and Unicode

Total characters161434
Distinct characters99
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique990 ?
Unique (%)62.9%

Sample

1st row2020 Summer Vintage Flamingo Print Pajamas Set Casual Loose T Shirt Top And Elastic Shorts Women Sleepwear Night Wear Loungewear Sets
2nd rowWomen's Casual Summer Sleeveless Sexy Mini Dress
3rd row2020 New Arrival Women Spring and Summer Beach Wear Long Thin Cardigan Open Front Kimono Green Leaf Printed Chiffon Cardigan S-5XL
4th rowHot Summer Cool T Shirt for Women Fashion Tops Bee Printed Letters Short Sleeve O Neck Cotton T-shirts Tops Tee Clothing
5th rowWomen Summer Shorts Lace Up Elastic Waistband Loose Thin Casual Pants Plus Size S-8XL
ValueCountFrequency (%)
New Fashion Summer Women Casual Dress Round Neck Loose Big Swing Skirt Sleeveless Soild Color Beach dress24
 
1.5%
Sexy Women's Summer Casual Sleeveless Evening Party Backless Beachwear Mini Dress12
 
0.8%
Summer Women s Fashion Lace Up Tie Pants Plus Size Casual High Waist Short Pants9
 
0.6%
Fabric Waist Trainer Vest Hot Shaper Summer Shaperwear Slimming Adjustable Sweat Belt Body Shaper9
 
0.6%
Summer Women Sling Dress V-neck Floral Pleated Strap Casual Pocket Large Dress9
 
0.6%
Women's Summer Fashion Spaghetti Strap Floral Print Tie Front Mini Dress Sexy Dress8
 
0.5%
Summer Fashion Trousers Women Leggings Ripped Pants Slim Pants Army Green Tights Pants7
 
0.4%
Women Summer Casual Loose Solid Color Vintage Overalls Strap Long Pants Jumpsuits Rompers Plus Size7
 
0.4%
Summer mens cool sport suit Sports Wear Casual Jogging Suits Short Sleeve Outfit Sets7
 
0.4%
Summer fashion female butterfly tank vest sleeveless round neck casual top6
 
0.4%
Other values (1193)1475
93.8%
2021-06-18T09:00:42.445651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
summer1327
 
5.2%
dress1031
 
4.1%
women1004
 
4.0%
fashion866
 
3.4%
casual847
 
3.4%
size595
 
2.4%
plus561
 
2.2%
sleeveless545
 
2.2%
loose452
 
1.8%
short432
 
1.7%
Other values (1509)17622
69.7%

Most occurring characters

ValueCountFrequency (%)
23954
14.8%
e15787
 
9.8%
s11679
 
7.2%
o9467
 
5.9%
r8098
 
5.0%
a7733
 
4.8%
i7566
 
4.7%
n6930
 
4.3%
S6756
 
4.2%
t6534
 
4.0%
Other values (89)56930
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter108681
67.3%
Uppercase Letter25654
 
15.9%
Space Separator23954
 
14.8%
Decimal Number1435
 
0.9%
Dash Punctuation955
 
0.6%
Other Punctuation579
 
0.4%
Open Punctuation57
 
< 0.1%
Close Punctuation57
 
< 0.1%
Math Symbol27
 
< 0.1%
Final Punctuation15
 
< 0.1%
Other values (4)20
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e15787
14.5%
s11679
10.7%
o9467
8.7%
r8098
 
7.5%
a7733
 
7.1%
i7566
 
7.0%
n6930
 
6.4%
t6534
 
6.0%
l6424
 
5.9%
m5311
 
4.9%
Other values (21)23152
21.3%
ValueCountFrequency (%)
S6756
26.3%
C2044
 
8.0%
P2030
 
7.9%
W1894
 
7.4%
T1865
 
7.3%
L1739
 
6.8%
D1510
 
5.9%
F1470
 
5.7%
B1425
 
5.6%
N743
 
2.9%
Other values (18)4178
16.3%
ValueCountFrequency (%)
'444
76.7%
/40
 
6.9%
,34
 
5.9%
"18
 
3.1%
&15
 
2.6%
:7
 
1.2%
.7
 
1.2%
;4
 
0.7%
!4
 
0.7%
\2
 
0.3%
Other values (3)4
 
0.7%
ValueCountFrequency (%)
2329
22.9%
0318
22.2%
5246
17.1%
1164
11.4%
8105
 
7.3%
989
 
6.2%
369
 
4.8%
449
 
3.4%
645
 
3.1%
721
 
1.5%
ValueCountFrequency (%)
(49
86.0%
7
 
12.3%
[1
 
1.8%
ValueCountFrequency (%)
)53
93.0%
3
 
5.3%
]1
 
1.8%
ValueCountFrequency (%)
~16
59.3%
+11
40.7%
ValueCountFrequency (%)
14
93.3%
1
 
6.7%
ValueCountFrequency (%)
7
70.0%
3
30.0%
ValueCountFrequency (%)
23954
100.0%
ValueCountFrequency (%)
-955
100.0%
ValueCountFrequency (%)
_8
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin134335
83.2%
Common27099
 
16.8%

Most frequent character per script

ValueCountFrequency (%)
e15787
 
11.8%
s11679
 
8.7%
o9467
 
7.0%
r8098
 
6.0%
a7733
 
5.8%
i7566
 
5.6%
n6930
 
5.2%
S6756
 
5.0%
t6534
 
4.9%
l6424
 
4.8%
Other values (49)47361
35.3%
ValueCountFrequency (%)
23954
88.4%
-955
 
3.5%
'444
 
1.6%
2329
 
1.2%
0318
 
1.2%
5246
 
0.9%
1164
 
0.6%
8105
 
0.4%
989
 
0.3%
369
 
0.3%
Other values (30)426
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII161388
> 99.9%
Punctuation25
 
< 0.1%
None20
 
< 0.1%
Letterlike Symbols1
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
23954
14.8%
e15787
 
9.8%
s11679
 
7.2%
o9467
 
5.9%
r8098
 
5.0%
a7733
 
4.8%
i7566
 
4.7%
n6930
 
4.3%
S6756
 
4.2%
t6534
 
4.0%
Other values (75)56884
35.2%
ValueCountFrequency (%)
7
35.0%
3
15.0%
ä3
15.0%
é2
 
10.0%
Ü1
 
5.0%
ö1
 
5.0%
ß1
 
5.0%
Ä1
 
5.0%
à1
 
5.0%
ValueCountFrequency (%)
14
56.0%
7
28.0%
3
 
12.0%
1
 
4.0%
ValueCountFrequency (%)
1
100.0%

listed_price
Real number (ℝ≥0)

Distinct127
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.325371901
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Memory size12.4 KiB
2021-06-18T09:00:42.601679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.656
Q15.81
median8
Q311
95-th percentile15
Maximum49
Range48
Interquartile range (IQR)5.19

Descriptive statistics

Standard deviation3.932029815
Coefficient of variation (CV)0.472294795
Kurtosis7.765125164
Mean8.325371901
Median Absolute Deviation (MAD)3
Skewness1.3158913
Sum13095.81
Variance15.46085847
MonotocityNot monotonic
2021-06-18T09:00:42.744333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8282
17.9%
11202
12.8%
7129
 
8.2%
9126
 
8.0%
6120
 
7.6%
1281
 
5.1%
580
 
5.1%
1457
 
3.6%
1354
 
3.4%
1642
 
2.7%
Other values (117)400
25.4%
ValueCountFrequency (%)
15
0.3%
1.652
 
0.1%
1.662
 
0.1%
1.671
 
0.1%
1.685
0.3%
ValueCountFrequency (%)
491
0.1%
271
0.1%
261
0.1%
251
0.1%
241
0.1%

retail_price
Real number (ℝ≥0)

Distinct104
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.28862047
Minimum1
Maximum252
Zeros0
Zeros (%)0.0%
Memory size12.4 KiB
2021-06-18T09:00:42.894897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median10
Q326
95-th percentile85
Maximum252
Range251
Interquartile range (IQR)19

Descriptive statistics

Standard deviation30.35786309
Coefficient of variation (CV)1.303549222
Kurtosis10.04536878
Mean23.28862047
Median Absolute Deviation (MAD)5
Skewness2.742709302
Sum36633
Variance921.5998513
MonotocityNot monotonic
2021-06-18T09:00:43.051479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7177
 
11.3%
6136
 
8.6%
10128
 
8.1%
5102
 
6.5%
1199
 
6.3%
890
 
5.7%
954
 
3.4%
450
 
3.2%
1747
 
3.0%
245
 
2.9%
Other values (94)645
41.0%
ValueCountFrequency (%)
11
 
0.1%
245
2.9%
337
 
2.4%
450
3.2%
5102
6.5%
ValueCountFrequency (%)
2522
 
0.1%
2501
 
0.1%
1691
 
0.1%
1685
0.3%
1594
0.3%

currency_buyer
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
EUR
1573 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4719
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEUR
2nd rowEUR
3rd rowEUR
4th rowEUR
5th rowEUR
ValueCountFrequency (%)
EUR1573
100.0%
2021-06-18T09:00:43.310802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-18T09:00:43.429468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
eur1573
100.0%

Most occurring characters

ValueCountFrequency (%)
E1573
33.3%
U1573
33.3%
R1573
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4719
100.0%

Most frequent character per category

ValueCountFrequency (%)
E1573
33.3%
U1573
33.3%
R1573
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin4719
100.0%

Most frequent character per script

ValueCountFrequency (%)
E1573
33.3%
U1573
33.3%
R1573
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4719
100.0%

Most frequent character per block

ValueCountFrequency (%)
E1573
33.3%
U1573
33.3%
R1573
33.3%

units_sold
Real number (ℝ≥0)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4339.005086
Minimum1
Maximum100000
Zeros0
Zeros (%)0.0%
Memory size12.4 KiB
2021-06-18T09:00:43.503277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50
Q1100
median1000
Q35000
95-th percentile20000
Maximum100000
Range99999
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation9356.539302
Coefficient of variation (CV)2.156378966
Kurtosis45.56805559
Mean4339.005086
Median Absolute Deviation (MAD)900
Skewness5.624840138
Sum6825255
Variance87544827.71
MonotocityNot monotonic
2021-06-18T09:00:43.604536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
100509
32.4%
1000405
25.7%
5000217
13.8%
10000177
 
11.3%
20000103
 
6.5%
5076
 
4.8%
1049
 
3.1%
5000017
 
1.1%
1000006
 
0.4%
84
 
0.3%
Other values (5)10
 
0.6%
ValueCountFrequency (%)
13
0.2%
22
0.1%
32
0.1%
61
 
0.1%
72
0.1%
ValueCountFrequency (%)
1000006
 
0.4%
5000017
 
1.1%
20000103
6.5%
10000177
11.3%
5000217
13.8%

uses_ad_boosts
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
0
892 
1
681 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1573
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1
ValueCountFrequency (%)
0892
56.7%
1681
43.3%
2021-06-18T09:00:43.844865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-18T09:00:43.925651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0892
56.7%
1681
43.3%

Most occurring characters

ValueCountFrequency (%)
0892
56.7%
1681
43.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1573
100.0%

Most frequent character per category

ValueCountFrequency (%)
0892
56.7%
1681
43.3%

Most occurring scripts

ValueCountFrequency (%)
Common1573
100.0%

Most frequent character per script

ValueCountFrequency (%)
0892
56.7%
1681
43.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1573
100.0%

Most frequent character per block

ValueCountFrequency (%)
0892
56.7%
1681
43.3%

rating
Real number (ℝ≥0)

Distinct192
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.820896376
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size12.4 KiB
2021-06-18T09:00:44.043334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13.55
median3.85
Q34.11
95-th percentile4.698
Maximum5
Range4
Interquartile range (IQR)0.56

Descriptive statistics

Standard deviation0.5153735991
Coefficient of variation (CV)0.134882904
Kurtosis2.735180424
Mean3.820896376
Median Absolute Deviation (MAD)0.28
Skewness-0.5309121947
Sum6010.27
Variance0.2656099466
MonotocityNot monotonic
2021-06-18T09:00:44.182968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
570
 
4.5%
444
 
2.8%
3.6733
 
2.1%
4.0722
 
1.4%
322
 
1.4%
3.6121
 
1.3%
3.821
 
1.3%
3.9620
 
1.3%
3.7519
 
1.2%
4.1419
 
1.2%
Other values (182)1282
81.5%
ValueCountFrequency (%)
13
 
0.2%
1.52
 
0.1%
29
0.6%
2.251
 
0.1%
2.332
 
0.1%
ValueCountFrequency (%)
570
4.5%
4.861
 
0.1%
4.831
 
0.1%
4.82
 
0.1%
4.754
 
0.3%

rating_count
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct761
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean889.6592498
Minimum0
Maximum20744
Zeros45
Zeros (%)2.9%
Memory size12.4 KiB
2021-06-18T09:00:44.334554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q124
median150
Q3855
95-th percentile3771.4
Maximum20744
Range20744
Interquartile range (IQR)831

Descriptive statistics

Standard deviation1983.928834
Coefficient of variation (CV)2.229987306
Kurtosis30.00764131
Mean889.6592498
Median Absolute Deviation (MAD)146
Skewness4.789467043
Sum1399434
Variance3935973.619
MonotocityNot monotonic
2021-06-18T09:00:44.463239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
045
 
2.9%
231
 
2.0%
428
 
1.8%
626
 
1.7%
1225
 
1.6%
323
 
1.5%
1020
 
1.3%
119
 
1.2%
819
 
1.2%
1317
 
1.1%
Other values (751)1320
83.9%
ValueCountFrequency (%)
045
2.9%
119
1.2%
231
2.0%
323
1.5%
428
1.8%
ValueCountFrequency (%)
207441
0.1%
184631
0.1%
183931
0.1%
179801
0.1%
174441
0.1%

rating_five_count
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct605
Distinct (%)39.6%
Missing45
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean442.2637435
Minimum0
Maximum11548
Zeros31
Zeros (%)2.0%
Memory size12.4 KiB
2021-06-18T09:00:44.590052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q112
median79
Q3413.5
95-th percentile2048.25
Maximum11548
Range11548
Interquartile range (IQR)401.5

Descriptive statistics

Standard deviation980.2032696
Coefficient of variation (CV)2.21633196
Kurtosis34.12450729
Mean442.2637435
Median Absolute Deviation (MAD)76
Skewness4.930986369
Sum675779
Variance960798.4497
MonotocityNot monotonic
2021-06-18T09:00:44.766583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149
 
3.1%
549
 
3.1%
344
 
2.8%
243
 
2.7%
434
 
2.2%
031
 
2.0%
926
 
1.7%
724
 
1.5%
821
 
1.3%
1721
 
1.3%
Other values (595)1186
75.4%
(Missing)45
 
2.9%
ValueCountFrequency (%)
031
2.0%
149
3.1%
243
2.7%
344
2.8%
434
2.2%
ValueCountFrequency (%)
115481
0.1%
111841
0.1%
82901
0.1%
75301
0.1%
73371
0.1%

rating_four_count
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct440
Distinct (%)28.8%
Missing45
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean179.5994764
Minimum0
Maximum4152
Zeros96
Zeros (%)6.1%
Memory size12.4 KiB
2021-06-18T09:00:44.896171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median31.5
Q3168.25
95-th percentile758.6
Maximum4152
Range4152
Interquartile range (IQR)163.25

Descriptive statistics

Standard deviation400.5162311
Coefficient of variation (CV)2.230052331
Kurtosis27.71305173
Mean179.5994764
Median Absolute Deviation (MAD)30.5
Skewness4.665102585
Sum274428
Variance160413.2514
MonotocityNot monotonic
2021-06-18T09:00:45.006601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
096
 
6.1%
191
 
5.8%
267
 
4.3%
353
 
3.4%
453
 
3.4%
552
 
3.3%
732
 
2.0%
1128
 
1.8%
627
 
1.7%
827
 
1.7%
Other values (430)1002
63.7%
(Missing)45
 
2.9%
ValueCountFrequency (%)
096
6.1%
191
5.8%
267
4.3%
353
3.4%
453
3.4%
ValueCountFrequency (%)
41521
0.1%
34831
0.1%
34041
0.1%
33511
0.1%
31911
0.1%

rating_three_count
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct384
Distinct (%)25.1%
Missing45
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean134.5497382
Minimum0
Maximum3658
Zeros138
Zeros (%)8.8%
Memory size12.4 KiB
2021-06-18T09:00:45.151216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median24
Q3129.25
95-th percentile574.6
Maximum3658
Range3658
Interquartile range (IQR)125.25

Descriptive statistics

Standard deviation311.6906559
Coefficient of variation (CV)2.316545985
Kurtosis35.54985044
Mean134.5497382
Median Absolute Deviation (MAD)24
Skewness5.174655967
Sum205592
Variance97151.06498
MonotocityNot monotonic
2021-06-18T09:00:45.294864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0138
 
8.8%
186
 
5.5%
279
 
5.0%
553
 
3.4%
351
 
3.2%
450
 
3.2%
636
 
2.3%
734
 
2.2%
1031
 
2.0%
825
 
1.6%
Other values (374)945
60.1%
(Missing)45
 
2.9%
ValueCountFrequency (%)
0138
8.8%
186
5.5%
279
5.0%
351
 
3.2%
450
 
3.2%
ValueCountFrequency (%)
36581
0.1%
30571
0.1%
29511
0.1%
29191
0.1%
26241
0.1%

rating_two_count
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct262
Distinct (%)17.1%
Missing45
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean63.71138743
Minimum0
Maximum2003
Zeros196
Zeros (%)12.5%
Memory size12.4 KiB
2021-06-18T09:00:45.429472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median11
Q362
95-th percentile275.55
Maximum2003
Range2003
Interquartile range (IQR)60

Descriptive statistics

Standard deviation151.343933
Coefficient of variation (CV)2.375461266
Kurtosis44.81767579
Mean63.71138743
Median Absolute Deviation (MAD)11
Skewness5.665096423
Sum97351
Variance22904.98607
MonotocityNot monotonic
2021-06-18T09:00:45.560530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0196
 
12.5%
1159
 
10.1%
284
 
5.3%
370
 
4.5%
456
 
3.6%
540
 
2.5%
638
 
2.4%
736
 
2.3%
830
 
1.9%
923
 
1.5%
Other values (252)796
50.6%
(Missing)45
 
2.9%
ValueCountFrequency (%)
0196
12.5%
1159
10.1%
284
5.3%
370
 
4.5%
456
 
3.6%
ValueCountFrequency (%)
20031
0.1%
17361
0.1%
14101
0.1%
13101
0.1%
11741
0.1%

rating_one_count
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct330
Distinct (%)21.6%
Missing45
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean95.73560209
Minimum0
Maximum2789
Zeros116
Zeros (%)7.4%
Memory size12.4 KiB
2021-06-18T09:00:45.967987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median20
Q394
95-th percentile396.65
Maximum2789
Range2789
Interquartile range (IQR)90

Descriptive statistics

Standard deviation214.0755444
Coefficient of variation (CV)2.236112164
Kurtosis41.82846709
Mean95.73560209
Median Absolute Deviation (MAD)19
Skewness5.383055991
Sum146284
Variance45828.33869
MonotocityNot monotonic
2021-06-18T09:00:46.085577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0116
 
7.4%
1116
 
7.4%
376
 
4.8%
273
 
4.6%
459
 
3.8%
746
 
2.9%
539
 
2.5%
632
 
2.0%
832
 
2.0%
927
 
1.7%
Other values (320)912
58.0%
(Missing)45
 
2.9%
ValueCountFrequency (%)
0116
7.4%
1116
7.4%
273
4.6%
376
4.8%
459
3.8%
ValueCountFrequency (%)
27891
0.1%
25591
0.1%
18461
0.1%
17361
0.1%
16001
0.1%

badges_count
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
0
1422 
1
 
138
2
 
11
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1573
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01422
90.4%
1138
 
8.8%
211
 
0.7%
32
 
0.1%
2021-06-18T09:00:46.351906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-18T09:00:46.418717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
01422
90.4%
1138
 
8.8%
211
 
0.7%
32
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01422
90.4%
1138
 
8.8%
211
 
0.7%
32
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1573
100.0%

Most frequent character per category

ValueCountFrequency (%)
01422
90.4%
1138
 
8.8%
211
 
0.7%
32
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1573
100.0%

Most frequent character per script

ValueCountFrequency (%)
01422
90.4%
1138
 
8.8%
211
 
0.7%
32
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1573
100.0%

Most frequent character per block

ValueCountFrequency (%)
01422
90.4%
1138
 
8.8%
211
 
0.7%
32
 
0.1%

badge_local_product
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
0
1544 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1573
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01544
98.2%
129
 
1.8%
2021-06-18T09:00:46.687006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-18T09:00:46.771774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
01544
98.2%
129
 
1.8%

Most occurring characters

ValueCountFrequency (%)
01544
98.2%
129
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1573
100.0%

Most frequent character per category

ValueCountFrequency (%)
01544
98.2%
129
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common1573
100.0%

Most frequent character per script

ValueCountFrequency (%)
01544
98.2%
129
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1573
100.0%

Most frequent character per block

ValueCountFrequency (%)
01544
98.2%
129
 
1.8%

badge_product_quality
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
0
1456 
1
 
117

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1573
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01456
92.6%
1117
 
7.4%
2021-06-18T09:00:46.975234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-18T09:00:47.040061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
01456
92.6%
1117
 
7.4%

Most occurring characters

ValueCountFrequency (%)
01456
92.6%
1117
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1573
100.0%

Most frequent character per category

ValueCountFrequency (%)
01456
92.6%
1117
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common1573
100.0%

Most frequent character per script

ValueCountFrequency (%)
01456
92.6%
1117
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1573
100.0%

Most frequent character per block

ValueCountFrequency (%)
01456
92.6%
1117
 
7.4%

badge_fast_shipping
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
0
1553 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1573
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01553
98.7%
120
 
1.3%
2021-06-18T09:00:47.246035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-18T09:00:47.310897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
01553
98.7%
120
 
1.3%

Most occurring characters

ValueCountFrequency (%)
01553
98.7%
120
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1573
100.0%

Most frequent character per category

ValueCountFrequency (%)
01553
98.7%
120
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common1573
100.0%

Most frequent character per script

ValueCountFrequency (%)
01553
98.7%
120
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1573
100.0%

Most frequent character per block

ValueCountFrequency (%)
01553
98.7%
120
 
1.3%

tags
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1230
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
Summer,Fashion,Necks,Skirts,Dress,Loose,Women's Fashion,Round neck,beach dress,sleeveless,Beach,Casual,Women
 
17
Summer,Sling,Dresses,Dress,V-neck,Casual,Pocket,Women's Fashion,Sleeveless dress,women dress,Floral,sleeveless,Women,loose dress,Pleated,casual dress
 
9
slimming,wasitcincher,Fashion,waistgirdle,slimmingcorset,Corset,Summer,Waist,waist trainer,Fashion Accessory,Vest,shaperwear,belt
 
8
Summer,short sleeve dress,neck dress,Necks,Sleeve,Beach,Dress,Loose,short sleeves,V-neck,Shorts,beach dress,Plus Size,Midi Dress,summer dress,Print,Pullovers,Women's Fashion,Casual,Women
 
7
Summer,Women Rompers,Plus Size,women long pants,linenjumpsuit,pants,Overalls,Loose,plussizejumpsuit,Women's Fashion,strappant,Long pants,Jumpsuits & Rompers,rompers womens jumpsuit,Vintage,Women,women Jumpsuit,Casual,jumpsuit
 
7
Other values (1225)
1525 

Length

Max length448
Median length165
Mean length169.1411316
Min length61

Characters and Unicode

Total characters266059
Distinct characters85
Distinct categories8 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1005 ?
Unique (%)63.9%

Sample

1st rowSummer,Fashion,womenunderwearsuit,printedpajamasset,womencasualshort,Women's Fashion,flamingo,loungewearset,Casual,Shirt,casualsleepwear,Shorts,flamingotshirt,Elastic,Vintage,Tops,tshirtandshortsset,Women,Sleepwear,Print,womenpajamasset,womennightwear,Pajamas,womensleepwearset
2nd rowMini,womens dresses,Summer,Patchwork,fashion dress,Dress,Mini dress,Women's Fashion,Women S Clothing,backless,party,summer dresses,sleeveless,sexy,Casual
3rd rowSummer,cardigan,women beachwear,chiffon,Sexy women,Coat,summercardigan,openfront,short sleeves,Swimsuit,Women's Fashion,leaf,Green,printed,Spring,longcardigan,Women,Beach,kimono
4th rowSummer,Shorts,Cotton,Cotton T Shirt,Sleeve,printedletterstop,Clothing,Tops,Necks,short sleeves,Women's Fashion,Women Clothing,printed,Women,tshirtforwomen,Fashion,T Shirts,Shirt
5th rowSummer,Plus Size,Lace,Casual pants,Bottom,pants,Loose,Women's Fashion,Shorts,Lace Up,Elastic,Casual,Women
ValueCountFrequency (%)
Summer,Fashion,Necks,Skirts,Dress,Loose,Women's Fashion,Round neck,beach dress,sleeveless,Beach,Casual,Women17
 
1.1%
Summer,Sling,Dresses,Dress,V-neck,Casual,Pocket,Women's Fashion,Sleeveless dress,women dress,Floral,sleeveless,Women,loose dress,Pleated,casual dress9
 
0.6%
slimming,wasitcincher,Fashion,waistgirdle,slimmingcorset,Corset,Summer,Waist,waist trainer,Fashion Accessory,Vest,shaperwear,belt8
 
0.5%
Summer,short sleeve dress,neck dress,Necks,Sleeve,Beach,Dress,Loose,short sleeves,V-neck,Shorts,beach dress,Plus Size,Midi Dress,summer dress,Print,Pullovers,Women's Fashion,Casual,Women7
 
0.4%
Summer,Women Rompers,Plus Size,women long pants,linenjumpsuit,pants,Overalls,Loose,plussizejumpsuit,Women's Fashion,strappant,Long pants,Jumpsuits & Rompers,rompers womens jumpsuit,Vintage,Women,women Jumpsuit,Casual,jumpsuit7
 
0.4%
Summer,Fashion,Necks,Beach,Dress,Loose,beach dress,Round neck,Women's Fashion,sleeveless,Skirts,Casual,Women7
 
0.4%
Summer,Leggings,Fashion,high waist,pants,slim,Women's Fashion,trousers,Green,Army,Women6
 
0.4%
pajamaset,Fashion,sexy pajamas for womens,silksleepwearforwomen,silksleepwear,pajamassuit,Casual,Women's Fashion,Summer,Sleepwear,pajamasforwomen,silksleepwearnightgown,silk,pajamassleepwear,women's pajamas,Women5
 
0.3%
Summer,Shorts,high waist shorts,high waist,Casual pants,pants,summer shorts,Waist,Slim Fit,Short pants,Women's Fashion,Plus Size,Lace Up,Women,Fashion,Casual,Lace5
 
0.3%
Mini,womens dresses,Summer,sleevele,Dress,Mini dress,Women's Fashion,Fashion,backless,party,sexy,summer dresses,Women S Clothing,Casual,sleeveless5
 
0.3%
Other values (1220)1497
95.2%
2021-06-18T09:00:47.645317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
summer,plus200
 
1.6%
168
 
1.4%
dress,women's162
 
1.3%
dress141
 
1.2%
t109
 
0.9%
sleeve104
 
0.8%
for95
 
0.8%
tank82
 
0.7%
fashion,plus81
 
0.7%
fashion,sleeveless78
 
0.6%
Other values (5417)11018
90.0%

Most occurring characters

ValueCountFrequency (%)
s29143
 
11.0%
e26515
 
10.0%
,25782
 
9.7%
o16754
 
6.3%
r13985
 
5.3%
n13871
 
5.2%
i13599
 
5.1%
a13211
 
5.0%
t12856
 
4.8%
10672
 
4.0%
Other values (75)89671
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter204942
77.0%
Other Punctuation27442
 
10.3%
Uppercase Letter22283
 
8.4%
Space Separator10672
 
4.0%
Dash Punctuation631
 
0.2%
Decimal Number80
 
< 0.1%
Other Letter5
 
< 0.1%
Connector Punctuation4
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
s29143
14.2%
e26515
12.9%
o16754
 
8.2%
r13985
 
6.8%
n13871
 
6.8%
i13599
 
6.6%
a13211
 
6.4%
t12856
 
6.3%
m10254
 
5.0%
l9595
 
4.7%
Other values (29)45159
22.0%
ValueCountFrequency (%)
S5555
24.9%
F3339
15.0%
W2866
12.9%
C1735
 
7.8%
T1568
 
7.0%
P1490
 
6.7%
D1253
 
5.6%
L890
 
4.0%
B789
 
3.5%
V645
 
2.9%
Other values (14)2153
 
9.7%
ValueCountFrequency (%)
225
31.2%
319
23.8%
49
 
11.2%
09
 
11.2%
18
 
10.0%
93
 
3.8%
53
 
3.8%
82
 
2.5%
72
 
2.5%
ValueCountFrequency (%)
,25782
94.0%
'1462
 
5.3%
&171
 
0.6%
#25
 
0.1%
/2
 
< 0.1%
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
ValueCountFrequency (%)
10672
100.0%
ValueCountFrequency (%)
-631
100.0%
ValueCountFrequency (%)
_4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin227213
85.4%
Common38829
 
14.6%
Cyrillic12
 
< 0.1%
Han4
 
< 0.1%
Hiragana1
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
s29143
12.8%
e26515
 
11.7%
o16754
 
7.4%
r13985
 
6.2%
n13871
 
6.1%
i13599
 
6.0%
a13211
 
5.8%
t12856
 
5.7%
m10254
 
4.5%
l9595
 
4.2%
Other values (41)67430
29.7%
ValueCountFrequency (%)
,25782
66.4%
10672
27.5%
'1462
 
3.8%
-631
 
1.6%
&171
 
0.4%
#25
 
0.1%
225
 
0.1%
319
 
< 0.1%
49
 
< 0.1%
09
 
< 0.1%
Other values (7)24
 
0.1%
ValueCountFrequency (%)
ш1
8.3%
о1
8.3%
р1
8.3%
т1
8.3%
ы1
8.3%
м1
8.3%
у1
8.3%
ж1
8.3%
с1
8.3%
к1
8.3%
Other values (2)2
16.7%
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII266041
> 99.9%
Cyrillic12
 
< 0.1%
CJK4
 
< 0.1%
Hiragana1
 
< 0.1%
None1
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
s29143
 
11.0%
e26515
 
10.0%
,25782
 
9.7%
o16754
 
6.3%
r13985
 
5.3%
n13871
 
5.2%
i13599
 
5.1%
a13211
 
5.0%
t12856
 
4.8%
10672
 
4.0%
Other values (57)89653
33.7%
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
ш1
8.3%
о1
8.3%
р1
8.3%
т1
8.3%
ы1
8.3%
м1
8.3%
у1
8.3%
ж1
8.3%
с1
8.3%
к1
8.3%
Other values (2)2
16.7%
ValueCountFrequency (%)
é1
100.0%

product_color
Categorical

HIGH CARDINALITY
MISSING

Distinct101
Distinct (%)6.6%
Missing41
Missing (%)2.6%
Memory size12.4 KiB
black
302 
white
254 
yellow
105 
blue
99 
pink
99 
Other values (96)
673 

Length

Max length19
Median length5
Mean length5.532637076
Min length3

Characters and Unicode

Total characters8476
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)2.5%

Sample

1st rowwhite
2nd rowgreen
3rd rowleopardprint
4th rowblack
5th rowyellow
ValueCountFrequency (%)
black302
19.2%
white254
16.1%
yellow105
 
6.7%
blue99
 
6.3%
pink99
 
6.3%
red93
 
5.9%
green90
 
5.7%
grey71
 
4.5%
purple53
 
3.4%
armygreen31
 
2.0%
Other values (91)335
21.3%
(Missing)41
 
2.6%
2021-06-18T09:00:47.970190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black321
19.8%
white277
17.1%
pink110
 
6.8%
yellow109
 
6.7%
green108
 
6.7%
blue108
 
6.7%
red98
 
6.0%
grey73
 
4.5%
purple53
 
3.3%
40
 
2.5%
Other values (64)323
19.9%

Most occurring characters

ValueCountFrequency (%)
e1291
15.2%
l881
 
10.4%
r587
 
6.9%
i521
 
6.1%
b513
 
6.1%
a496
 
5.9%
k486
 
5.7%
w433
 
5.1%
n392
 
4.6%
c369
 
4.4%
Other values (23)2507
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8330
98.3%
Space Separator88
 
1.0%
Other Punctuation40
 
0.5%
Uppercase Letter15
 
0.2%
Dash Punctuation3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e1291
15.5%
l881
 
10.6%
r587
 
7.0%
i521
 
6.3%
b513
 
6.2%
a496
 
6.0%
k486
 
5.8%
w433
 
5.2%
n392
 
4.7%
c369
 
4.4%
Other values (13)2361
28.3%
ValueCountFrequency (%)
B4
26.7%
W3
20.0%
R2
13.3%
A2
13.3%
P2
13.3%
E1
 
6.7%
D1
 
6.7%
ValueCountFrequency (%)
88
100.0%
ValueCountFrequency (%)
&40
100.0%
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8345
98.5%
Common131
 
1.5%

Most frequent character per script

ValueCountFrequency (%)
e1291
15.5%
l881
 
10.6%
r587
 
7.0%
i521
 
6.2%
b513
 
6.1%
a496
 
5.9%
k486
 
5.8%
w433
 
5.2%
n392
 
4.7%
c369
 
4.4%
Other values (20)2376
28.5%
ValueCountFrequency (%)
88
67.2%
&40
30.5%
-3
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8476
100.0%

Most frequent character per block

ValueCountFrequency (%)
e1291
15.2%
l881
 
10.4%
r587
 
6.9%
i521
 
6.1%
b513
 
6.1%
a496
 
5.9%
k486
 
5.7%
w433
 
5.1%
n392
 
4.6%
c369
 
4.4%
Other values (23)2507
29.6%

product_variation_size_id
Categorical

HIGH CARDINALITY

Distinct106
Distinct (%)6.8%
Missing14
Missing (%)0.9%
Memory size12.4 KiB
S
641 
XS
356 
M
200 
XXS
100 
L
 
49
Other values (101)
213 

Length

Max length28
Median length1
Mean length1.934573445
Min length1

Characters and Unicode

Total characters3016
Distinct characters62
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)4.0%

Sample

1st rowM
2nd rowXS
3rd rowXS
4th rowM
5th rowS
ValueCountFrequency (%)
S641
40.8%
XS356
22.6%
M200
 
12.7%
XXS100
 
6.4%
L49
 
3.1%
S.18
 
1.1%
XL17
 
1.1%
XXL15
 
1.0%
XXXS6
 
0.4%
XS.5
 
0.3%
Other values (96)152
 
9.7%
(Missing)14
 
0.9%
2021-06-18T09:00:48.241035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s679
41.3%
xs365
22.2%
m206
 
12.5%
xxs104
 
6.3%
l51
 
3.1%
size25
 
1.5%
xl18
 
1.1%
xxl15
 
0.9%
xxxs6
 
0.4%
16
 
0.4%
Other values (110)171
 
10.4%

Most occurring characters

ValueCountFrequency (%)
S1221
40.5%
X691
22.9%
M210
 
7.0%
L114
 
3.8%
90
 
3.0%
i54
 
1.8%
e54
 
1.8%
.38
 
1.3%
z36
 
1.2%
331
 
1.0%
Other values (52)477
 
15.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2320
76.9%
Lowercase Letter362
 
12.0%
Decimal Number154
 
5.1%
Space Separator90
 
3.0%
Other Punctuation44
 
1.5%
Dash Punctuation28
 
0.9%
Open Punctuation9
 
0.3%
Close Punctuation9
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
i54
14.9%
e54
14.9%
z36
9.9%
s24
 
6.6%
c24
 
6.6%
a23
 
6.4%
t21
 
5.8%
m17
 
4.7%
o16
 
4.4%
n15
 
4.1%
Other values (14)78
21.5%
ValueCountFrequency (%)
S1221
52.6%
X691
29.8%
M210
 
9.1%
L114
 
4.9%
E14
 
0.6%
I11
 
0.5%
U10
 
0.4%
Z9
 
0.4%
P8
 
0.3%
B6
 
0.3%
Other values (11)26
 
1.1%
ValueCountFrequency (%)
331
20.1%
024
15.6%
222
14.3%
119
12.3%
518
11.7%
416
10.4%
88
 
5.2%
67
 
4.5%
95
 
3.2%
74
 
2.6%
ValueCountFrequency (%)
.38
86.4%
/3
 
6.8%
&3
 
6.8%
ValueCountFrequency (%)
-28
100.0%
ValueCountFrequency (%)
90
100.0%
ValueCountFrequency (%)
(9
100.0%
ValueCountFrequency (%)
)9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2682
88.9%
Common334
 
11.1%

Most frequent character per script

ValueCountFrequency (%)
S1221
45.5%
X691
25.8%
M210
 
7.8%
L114
 
4.3%
i54
 
2.0%
e54
 
2.0%
z36
 
1.3%
s24
 
0.9%
c24
 
0.9%
a23
 
0.9%
Other values (35)231
 
8.6%
ValueCountFrequency (%)
90
26.9%
.38
11.4%
331
 
9.3%
-28
 
8.4%
024
 
7.2%
222
 
6.6%
119
 
5.7%
518
 
5.4%
416
 
4.8%
(9
 
2.7%
Other values (7)39
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3016
100.0%

Most frequent character per block

ValueCountFrequency (%)
S1221
40.5%
X691
22.9%
M210
 
7.0%
L114
 
3.8%
90
 
3.0%
i54
 
1.8%
e54
 
1.8%
.38
 
1.3%
z36
 
1.2%
331
 
1.0%
Other values (52)477
 
15.8%

product_variation_inventory
Real number (ℝ≥0)

Distinct48
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.08137317
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Memory size12.4 KiB
2021-06-18T09:00:48.401438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median50
Q350
95-th percentile50
Maximum50
Range49
Interquartile range (IQR)44

Descriptive statistics

Standard deviation21.35313744
Coefficient of variation (CV)0.6454731286
Kurtosis-1.574971382
Mean33.08137317
Median Absolute Deviation (MAD)0
Skewness-0.5656531028
Sum52037
Variance455.9564785
MonotocityNot monotonic
2021-06-18T09:00:48.545246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
50907
57.7%
1152
 
9.7%
281
 
5.1%
574
 
4.7%
352
 
3.3%
1040
 
2.5%
425
 
1.6%
922
 
1.4%
618
 
1.1%
718
 
1.1%
Other values (38)184
 
11.7%
ValueCountFrequency (%)
1152
9.7%
281
5.1%
352
 
3.3%
425
 
1.6%
574
4.7%
ValueCountFrequency (%)
50907
57.7%
499
 
0.6%
484
 
0.3%
474
 
0.3%
466
 
0.4%

shipping_option_name
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
Livraison standard
1508 
Standard Shipping
 
21
Envio Padrão
 
9
Expediere Standard
 
6
Envío normal
 
5
Other values (10)
 
24

Length

Max length23
Median length18
Mean length17.92180547
Min length12

Characters and Unicode

Total characters28191
Distinct characters77
Distinct categories6 ?
Distinct scripts6 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowLivraison standard
2nd rowLivraison standard
3rd rowLivraison standard
4th rowLivraison standard
5th rowLivraison standard
ValueCountFrequency (%)
Livraison standard1508
95.9%
Standard Shipping21
 
1.3%
Envio Padrão9
 
0.6%
Expediere Standard6
 
0.4%
Envío normal5
 
0.3%
الشحن القياسي4
 
0.3%
Standardowa wysyłka3
 
0.2%
Standardversand3
 
0.2%
Livraison Express3
 
0.2%
Стандартная доставка3
 
0.2%
Other values (5)8
 
0.5%
2021-06-18T09:00:48.872004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
standard1537
48.9%
livraison1511
48.1%
shipping21
 
0.7%
envio9
 
0.3%
padrão9
 
0.3%
expediere6
 
0.2%
envío5
 
0.2%
normal5
 
0.2%
الشحن4
 
0.1%
القياسي4
 
0.1%
Other values (12)29
 
0.9%

Most occurring characters

ValueCountFrequency (%)
a4626
16.4%
d3110
11.0%
n3103
11.0%
i3085
10.9%
r3085
10.9%
s3036
10.8%
1567
 
5.6%
t1547
 
5.5%
o1545
 
5.5%
v1528
 
5.4%
Other values (67)1959
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24908
88.4%
Uppercase Letter1607
 
5.7%
Space Separator1567
 
5.6%
Other Letter95
 
0.3%
Nonspacing Mark11
 
< 0.1%
Spacing Mark3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a4626
18.6%
d3110
12.5%
n3103
12.5%
i3085
12.4%
r3085
12.4%
s3036
12.2%
t1547
 
6.2%
o1545
 
6.2%
v1528
 
6.1%
p54
 
0.2%
Other values (24)189
 
0.8%
ValueCountFrequency (%)
ا12
 
12.6%
ل8
 
8.4%
ي8
 
8.4%
8
 
8.4%
ش4
 
4.2%
ح4
 
4.2%
ن4
 
4.2%
ق4
 
4.2%
س4
 
4.2%
4
 
4.2%
Other values (18)35
36.8%
ValueCountFrequency (%)
2
18.2%
2
18.2%
2
18.2%
2
18.2%
1
9.1%
1
9.1%
1
9.1%
ValueCountFrequency (%)
L1511
94.0%
S58
 
3.6%
E24
 
1.5%
P9
 
0.6%
С3
 
0.2%
G2
 
0.1%
ValueCountFrequency (%)
1567
100.0%
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin26458
93.9%
Common1567
 
5.6%
Cyrillic57
 
0.2%
Arabic48
 
0.2%
Thai38
 
0.1%
Khmer23
 
0.1%

Most frequent character per script

ValueCountFrequency (%)
a4626
17.5%
d3110
11.8%
n3103
11.7%
i3085
11.7%
r3085
11.7%
s3036
11.5%
t1547
 
5.8%
o1545
 
5.8%
v1528
 
5.8%
L1511
 
5.7%
Other values (19)282
 
1.1%
ValueCountFrequency (%)
3
13.0%
2
 
8.7%
2
 
8.7%
2
 
8.7%
2
 
8.7%
2
 
8.7%
2
 
8.7%
1
 
4.3%
1
 
4.3%
1
 
4.3%
Other values (5)5
21.7%
ValueCountFrequency (%)
8
21.1%
4
10.5%
4
10.5%
4
10.5%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
Other values (3)6
15.8%
ValueCountFrequency (%)
а15
26.3%
т9
15.8%
н6
 
10.5%
д6
 
10.5%
С3
 
5.3%
р3
 
5.3%
я3
 
5.3%
о3
 
5.3%
с3
 
5.3%
в3
 
5.3%
ValueCountFrequency (%)
ا12
25.0%
ل8
16.7%
ي8
16.7%
ش4
 
8.3%
ح4
 
8.3%
ن4
 
8.3%
ق4
 
8.3%
س4
 
8.3%
ValueCountFrequency (%)
1567
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII28005
99.3%
Cyrillic57
 
0.2%
Arabic48
 
0.2%
Thai38
 
0.1%
Khmer23
 
0.1%
None20
 
0.1%

Most frequent character per block

ValueCountFrequency (%)
a4626
16.5%
d3110
11.1%
n3103
11.1%
i3085
11.0%
r3085
11.0%
s3036
10.8%
1567
 
5.6%
t1547
 
5.5%
o1545
 
5.5%
v1528
 
5.5%
Other values (16)1773
 
6.3%
ValueCountFrequency (%)
ã9
45.0%
í5
25.0%
ł4
20.0%
ö2
 
10.0%
ValueCountFrequency (%)
ا12
25.0%
ل8
16.7%
ي8
16.7%
ش4
 
8.3%
ح4
 
8.3%
ن4
 
8.3%
ق4
 
8.3%
س4
 
8.3%
ValueCountFrequency (%)
8
21.1%
4
10.5%
4
10.5%
4
10.5%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
Other values (3)6
15.8%
ValueCountFrequency (%)
3
13.0%
2
 
8.7%
2
 
8.7%
2
 
8.7%
2
 
8.7%
2
 
8.7%
2
 
8.7%
1
 
4.3%
1
 
4.3%
1
 
4.3%
Other values (5)5
21.7%
ValueCountFrequency (%)
а15
26.3%
т9
15.8%
н6
 
10.5%
д6
 
10.5%
С3
 
5.3%
р3
 
5.3%
я3
 
5.3%
о3
 
5.3%
с3
 
5.3%
в3
 
5.3%

shipping_option_price
Real number (ℝ≥0)

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.345200254
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size12.4 KiB
2021-06-18T09:00:48.967878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.024371048
Coefficient of variation (CV)0.4367947029
Kurtosis6.534053273
Mean2.345200254
Median Absolute Deviation (MAD)1
Skewness1.365252463
Sum3689
Variance1.049336044
MonotocityNot monotonic
2021-06-18T09:00:49.044432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2619
39.4%
3520
33.1%
1308
19.6%
476
 
4.8%
532
 
2.0%
612
 
0.8%
75
 
0.3%
121
 
0.1%
ValueCountFrequency (%)
1308
19.6%
2619
39.4%
3520
33.1%
476
 
4.8%
532
 
2.0%
ValueCountFrequency (%)
121
 
0.1%
75
 
0.3%
612
 
0.8%
532
2.0%
476
4.8%

shipping_is_express
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
0
1569 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1573
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01569
99.7%
14
 
0.3%
2021-06-18T09:00:49.263247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-18T09:00:49.339725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
01569
99.7%
14
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01569
99.7%
14
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1573
100.0%

Most frequent character per category

ValueCountFrequency (%)
01569
99.7%
14
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1573
100.0%

Most frequent character per script

ValueCountFrequency (%)
01569
99.7%
14
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1573
100.0%

Most frequent character per block

ValueCountFrequency (%)
01569
99.7%
14
 
0.3%

countries_shipped_to
Real number (ℝ≥0)

Distinct94
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.45645264
Minimum6
Maximum140
Zeros0
Zeros (%)0.0%
Memory size12.4 KiB
2021-06-18T09:00:49.418443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile18
Q131
median40
Q343
95-th percentile72.4
Maximum140
Range134
Interquartile range (IQR)12

Descriptive statistics

Standard deviation20.30120308
Coefficient of variation (CV)0.501803835
Kurtosis11.38391925
Mean40.45645264
Median Absolute Deviation (MAD)5
Skewness2.961890329
Sum63638
Variance412.1388467
MonotocityNot monotonic
2021-06-18T09:00:49.566216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41171
 
10.9%
43170
 
10.8%
40106
 
6.7%
3876
 
4.8%
3664
 
4.1%
3561
 
3.9%
4257
 
3.6%
3943
 
2.7%
2539
 
2.5%
3738
 
2.4%
Other values (84)748
47.6%
ValueCountFrequency (%)
61
 
0.1%
86
0.4%
94
0.3%
107
0.4%
112
 
0.1%
ValueCountFrequency (%)
1403
 
0.2%
13914
0.9%
1389
0.6%
1373
 
0.2%
1352
 
0.1%

inventory_total
Real number (ℝ≥0)

Distinct10
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.82136046
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Memory size12.4 KiB
2021-06-18T09:00:49.691537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50
Q150
median50
Q350
95-th percentile50
Maximum50
Range49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.562799326
Coefficient of variation (CV)0.05143977007
Kurtosis287.2634299
Mean49.82136046
Median Absolute Deviation (MAD)0
Skewness-16.47787667
Sum78369
Variance6.567940387
MonotocityNot monotonic
2021-06-18T09:00:49.785916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
501563
99.4%
22
 
0.1%
11
 
0.1%
91
 
0.1%
241
 
0.1%
301
 
0.1%
361
 
0.1%
371
 
0.1%
381
 
0.1%
401
 
0.1%
ValueCountFrequency (%)
11
0.1%
22
0.1%
91
0.1%
241
0.1%
301
0.1%
ValueCountFrequency (%)
501563
99.4%
401
 
0.1%
381
 
0.1%
371
 
0.1%
361
 
0.1%

has_urgency_banner
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
nan
1100 
1.0
473 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4719
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th rownan
5th row1.0
ValueCountFrequency (%)
nan1100
69.9%
1.0473
30.1%
2021-06-18T09:00:50.005260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-18T09:00:50.078352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
nan1100
69.9%
1.0473
30.1%

Most occurring characters

ValueCountFrequency (%)
n2200
46.6%
a1100
23.3%
1473
 
10.0%
.473
 
10.0%
0473
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3300
69.9%
Decimal Number946
 
20.0%
Other Punctuation473
 
10.0%

Most frequent character per category

ValueCountFrequency (%)
1473
50.0%
0473
50.0%
ValueCountFrequency (%)
n2200
66.7%
a1100
33.3%
ValueCountFrequency (%)
.473
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3300
69.9%
Common1419
30.1%

Most frequent character per script

ValueCountFrequency (%)
1473
33.3%
.473
33.3%
0473
33.3%
ValueCountFrequency (%)
n2200
66.7%
a1100
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4719
100.0%

Most frequent character per block

ValueCountFrequency (%)
n2200
46.6%
a1100
23.3%
1473
 
10.0%
.473
 
10.0%
0473
 
10.0%

urgency_text
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.4%
Missing1100
Missing (%)69.9%
Memory size12.4 KiB
Quantité limitée !
472 
Réduction sur les achats en gros
 
1

Length

Max length32
Median length18
Mean length18.02959831
Min length18

Characters and Unicode

Total characters8528
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowQuantité limitée !
2nd rowQuantité limitée !
3rd rowQuantité limitée !
4th rowQuantité limitée !
5th rowQuantité limitée !
ValueCountFrequency (%)
Quantité limitée !472
30.0%
Réduction sur les achats en gros1
 
0.1%
(Missing)1100
69.9%
2021-06-18T09:00:50.238157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-18T09:00:50.311453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
472
33.2%
quantité472
33.2%
limitée472
33.2%
achats1
 
0.1%
en1
 
0.1%
les1
 
0.1%
gros1
 
0.1%
réduction1
 
0.1%
sur1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
t1418
16.6%
i1417
16.6%
949
11.1%
é945
11.1%
u474
 
5.6%
a474
 
5.6%
n474
 
5.6%
e474
 
5.6%
l473
 
5.5%
Q472
 
5.5%
Other values (10)958
11.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6634
77.8%
Space Separator949
 
11.1%
Uppercase Letter473
 
5.5%
Other Punctuation472
 
5.5%

Most frequent character per category

ValueCountFrequency (%)
t1418
21.4%
i1417
21.4%
é945
14.2%
u474
 
7.1%
a474
 
7.1%
n474
 
7.1%
e474
 
7.1%
l473
 
7.1%
m472
 
7.1%
s4
 
0.1%
Other values (6)9
 
0.1%
ValueCountFrequency (%)
Q472
99.8%
R1
 
0.2%
ValueCountFrequency (%)
949
100.0%
ValueCountFrequency (%)
!472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7107
83.3%
Common1421
 
16.7%

Most frequent character per script

ValueCountFrequency (%)
t1418
20.0%
i1417
19.9%
é945
13.3%
u474
 
6.7%
a474
 
6.7%
n474
 
6.7%
e474
 
6.7%
l473
 
6.7%
Q472
 
6.6%
m472
 
6.6%
Other values (8)14
 
0.2%
ValueCountFrequency (%)
949
66.8%
!472
33.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII7583
88.9%
None945
 
11.1%

Most frequent character per block

ValueCountFrequency (%)
t1418
18.7%
i1417
18.7%
949
12.5%
u474
 
6.3%
a474
 
6.3%
n474
 
6.3%
e474
 
6.3%
l473
 
6.2%
Q472
 
6.2%
m472
 
6.2%
Other values (9)486
 
6.4%
ValueCountFrequency (%)
é945
100.0%

origin_country
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.4%
Missing17
Missing (%)1.1%
Memory size12.4 KiB
CN
1516 
US
 
31
VE
 
5
SG
 
2
AT
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3112
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowCN
2nd rowCN
3rd rowCN
4th rowCN
5th rowCN
ValueCountFrequency (%)
CN1516
96.4%
US31
 
2.0%
VE5
 
0.3%
SG2
 
0.1%
AT1
 
0.1%
GB1
 
0.1%
(Missing)17
 
1.1%
2021-06-18T09:00:50.505883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-18T09:00:50.576446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
cn1516
97.4%
us31
 
2.0%
ve5
 
0.3%
sg2
 
0.1%
gb1
 
0.1%
at1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
C1516
48.7%
N1516
48.7%
S33
 
1.1%
U31
 
1.0%
V5
 
0.2%
E5
 
0.2%
G3
 
0.1%
A1
 
< 0.1%
T1
 
< 0.1%
B1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3112
100.0%

Most frequent character per category

ValueCountFrequency (%)
C1516
48.7%
N1516
48.7%
S33
 
1.1%
U31
 
1.0%
V5
 
0.2%
E5
 
0.2%
G3
 
0.1%
A1
 
< 0.1%
T1
 
< 0.1%
B1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin3112
100.0%

Most frequent character per script

ValueCountFrequency (%)
C1516
48.7%
N1516
48.7%
S33
 
1.1%
U31
 
1.0%
V5
 
0.2%
E5
 
0.2%
G3
 
0.1%
A1
 
< 0.1%
T1
 
< 0.1%
B1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3112
100.0%

Most frequent character per block

ValueCountFrequency (%)
C1516
48.7%
N1516
48.7%
S33
 
1.1%
U31
 
1.0%
V5
 
0.2%
E5
 
0.2%
G3
 
0.1%
A1
 
< 0.1%
T1
 
< 0.1%
B1
 
< 0.1%

merchant_title
Categorical

HIGH CARDINALITY

Distinct958
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
guangzhouweishiweifushiyouxiangongsi
 
15
Suyi Technology
 
12
sjhdstoer
 
9
Sangboo Store
 
8
shuilingjiao international trade company
 
8
Other values (953)
1521 

Length

Max length51
Median length11
Mean length12.26636999
Min length2

Characters and Unicode

Total characters19295
Distinct characters75
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique635 ?
Unique (%)40.4%

Sample

1st rowzgrdejia
2nd rowSaraHouse
3rd rowhxt520
4th rowallenfan
5th rowyoungpeopleshop
ValueCountFrequency (%)
guangzhouweishiweifushiyouxiangongsi15
 
1.0%
Suyi Technology12
 
0.8%
sjhdstoer9
 
0.6%
Sangboo Store8
 
0.5%
shuilingjiao international trade company8
 
0.5%
Cenic Beauty8
 
0.5%
Pentiumhorse7
 
0.4%
snowgirl6
 
0.4%
witkey BL6
 
0.4%
sklioppp6
 
0.4%
Other values (948)1488
94.6%
2021-06-18T09:00:50.860641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fashion57
 
2.6%
store40
 
1.8%
international22
 
1.0%
technology17
 
0.8%
boutique17
 
0.8%
shop16
 
0.7%
the15
 
0.7%
guangzhouweishiweifushiyouxiangongsi15
 
0.7%
co.,ltd14
 
0.6%
ltd14
 
0.6%
Other values (1145)1996
89.8%

Most occurring characters

ValueCountFrequency (%)
i1530
 
7.9%
n1529
 
7.9%
a1462
 
7.6%
o1308
 
6.8%
e1283
 
6.6%
u876
 
4.5%
s828
 
4.3%
h822
 
4.3%
g799
 
4.1%
l706
 
3.7%
Other values (65)8152
42.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15639
81.1%
Uppercase Letter1982
 
10.3%
Decimal Number833
 
4.3%
Space Separator665
 
3.4%
Other Punctuation124
 
0.6%
Connector Punctuation37
 
0.2%
Dash Punctuation10
 
0.1%
Math Symbol2
 
< 0.1%
Initial Punctuation1
 
< 0.1%
Open Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
i1530
 
9.8%
n1529
 
9.8%
a1462
 
9.3%
o1308
 
8.4%
e1283
 
8.2%
u876
 
5.6%
s828
 
5.3%
h822
 
5.3%
g799
 
5.1%
l706
 
4.5%
Other values (16)4496
28.7%
ValueCountFrequency (%)
S248
 
12.5%
L148
 
7.5%
A124
 
6.3%
H121
 
6.1%
T118
 
6.0%
O106
 
5.3%
F97
 
4.9%
M93
 
4.7%
C89
 
4.5%
N86
 
4.3%
Other values (16)752
37.9%
ValueCountFrequency (%)
1142
17.0%
6130
15.6%
0112
13.4%
897
11.6%
291
10.9%
565
7.8%
962
7.4%
348
 
5.8%
446
 
5.5%
740
 
4.8%
ValueCountFrequency (%)
.74
59.7%
'19
 
15.3%
,17
 
13.7%
@7
 
5.6%
&4
 
3.2%
!3
 
2.4%
ValueCountFrequency (%)
665
100.0%
ValueCountFrequency (%)
_37
100.0%
ValueCountFrequency (%)
~2
100.0%
ValueCountFrequency (%)
-10
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
(1
100.0%
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17621
91.3%
Common1674
 
8.7%

Most frequent character per script

ValueCountFrequency (%)
i1530
 
8.7%
n1529
 
8.7%
a1462
 
8.3%
o1308
 
7.4%
e1283
 
7.3%
u876
 
5.0%
s828
 
4.7%
h822
 
4.7%
g799
 
4.5%
l706
 
4.0%
Other values (42)6478
36.8%
ValueCountFrequency (%)
665
39.7%
1142
 
8.5%
6130
 
7.8%
0112
 
6.7%
897
 
5.8%
291
 
5.4%
.74
 
4.4%
565
 
3.9%
962
 
3.7%
348
 
2.9%
Other values (13)188
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII19294
> 99.9%
Punctuation1
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
i1530
 
7.9%
n1529
 
7.9%
a1462
 
7.6%
o1308
 
6.8%
e1283
 
6.6%
u876
 
4.5%
s828
 
4.3%
h822
 
4.3%
g799
 
4.1%
l706
 
3.7%
Other values (64)8151
42.2%
ValueCountFrequency (%)
1
100.0%

merchant_name
Categorical

HIGH CARDINALITY

Distinct957
Distinct (%)61.0%
Missing4
Missing (%)0.3%
Memory size12.4 KiB
广州唯适唯服饰有限公司
 
15
greatexpectationstechnology
 
12
sjhdstoer
 
9
sangboostore
 
8
shuilingjiaointernationaltradecompany
 
8
Other values (952)
1517 

Length

Max length52
Median length11
Mean length11.7418738
Min length2

Characters and Unicode

Total characters18423
Distinct characters151
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique635 ?
Unique (%)40.5%

Sample

1st rowzgrdejia
2nd rowsarahouse
3rd rowhxt520
4th rowallenfan
5th rowhappyhorses
ValueCountFrequency (%)
广州唯适唯服饰有限公司15
 
1.0%
greatexpectationstechnology12
 
0.8%
sjhdstoer9
 
0.6%
sangboostore8
 
0.5%
shuilingjiaointernationaltradecompany8
 
0.5%
cenicbeauty8
 
0.5%
pentiumhorse7
 
0.4%
sarahouse6
 
0.4%
fengjinying6
 
0.4%
witkeybl6
 
0.4%
Other values (947)1484
94.3%
2021-06-18T09:00:51.159904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
广州唯适唯服饰有限公司15
 
1.0%
greatexpectationstechnology12
 
0.8%
sjhdstoer9
 
0.6%
cenicbeauty8
 
0.5%
sangboostore8
 
0.5%
shuilingjiaointernationaltradecompany8
 
0.5%
pentiumhorse7
 
0.4%
sklioppp6
 
0.4%
witkeybl6
 
0.4%
zuilangmands6
 
0.4%
Other values (947)1484
94.6%

Most occurring characters

ValueCountFrequency (%)
a1566
 
8.5%
n1491
 
8.1%
i1453
 
7.9%
e1364
 
7.4%
o1318
 
7.2%
s971
 
5.3%
l859
 
4.7%
h826
 
4.5%
t795
 
4.3%
g776
 
4.2%
Other values (141)7004
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16742
90.9%
Decimal Number1116
 
6.1%
Other Letter533
 
2.9%
Connector Punctuation32
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
37
 
6.9%
34
 
6.4%
34
 
6.4%
34
 
6.4%
30
 
5.6%
25
 
4.7%
23
 
4.3%
广18
 
3.4%
17
 
3.2%
15
 
2.8%
Other values (104)266
49.9%
ValueCountFrequency (%)
a1566
 
9.4%
n1491
 
8.9%
i1453
 
8.7%
e1364
 
8.1%
o1318
 
7.9%
s971
 
5.8%
l859
 
5.1%
h826
 
4.9%
t795
 
4.7%
g776
 
4.6%
Other values (16)5323
31.8%
ValueCountFrequency (%)
1195
17.5%
6159
14.2%
0154
13.8%
8128
11.5%
2119
10.7%
590
8.1%
980
7.2%
367
 
6.0%
466
 
5.9%
758
 
5.2%
ValueCountFrequency (%)
_32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16742
90.9%
Common1148
 
6.2%
Han533
 
2.9%

Most frequent character per script

ValueCountFrequency (%)
37
 
6.9%
34
 
6.4%
34
 
6.4%
34
 
6.4%
30
 
5.6%
25
 
4.7%
23
 
4.3%
广18
 
3.4%
17
 
3.2%
15
 
2.8%
Other values (104)266
49.9%
ValueCountFrequency (%)
a1566
 
9.4%
n1491
 
8.9%
i1453
 
8.7%
e1364
 
8.1%
o1318
 
7.9%
s971
 
5.8%
l859
 
5.1%
h826
 
4.9%
t795
 
4.7%
g776
 
4.6%
Other values (16)5323
31.8%
ValueCountFrequency (%)
1195
17.0%
6159
13.9%
0154
13.4%
8128
11.1%
2119
10.4%
590
7.8%
980
7.0%
367
 
5.8%
466
 
5.7%
758
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII17890
97.1%
CJK533
 
2.9%

Most frequent character per block

ValueCountFrequency (%)
a1566
 
8.8%
n1491
 
8.3%
i1453
 
8.1%
e1364
 
7.6%
o1318
 
7.4%
s971
 
5.4%
l859
 
4.8%
h826
 
4.6%
t795
 
4.4%
g776
 
4.3%
Other values (27)6471
36.2%
ValueCountFrequency (%)
37
 
6.9%
34
 
6.4%
34
 
6.4%
34
 
6.4%
30
 
5.6%
25
 
4.7%
23
 
4.3%
广18
 
3.4%
17
 
3.2%
15
 
2.8%
Other values (104)266
49.9%

merchant_info_subtitle
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1058
Distinct (%)67.3%
Missing1
Missing (%)0.1%
Memory size12.4 KiB
83 % avis positifs (32,168 notes)
 
14
86 % avis positifs (12,309 notes)
 
11
87 % avis positifs (42,919 notes)
 
8
85 % avis positifs (80,093 notes)
 
7
86 % avis positifs (65,189 notes)
 
6
Other values (1053)
1526 

Length

Max length54
Median length32
Mean length28.80788804
Min length9

Characters and Unicode

Total characters45286
Distinct characters102
Distinct categories10 ?
Distinct scripts6 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique742 ?
Unique (%)47.2%

Sample

1st row(568 notes)
2nd row83 % avis positifs (17,752 notes)
3rd row86 % avis positifs (295 notes)
4th row(23,832 notes)
5th row85 % avis positifs (14,482 notes)
ValueCountFrequency (%)
83 % avis positifs (32,168 notes)14
 
0.9%
86 % avis positifs (12,309 notes)11
 
0.7%
87 % avis positifs (42,919 notes)8
 
0.5%
85 % avis positifs (80,093 notes)7
 
0.4%
86 % avis positifs (65,189 notes)6
 
0.4%
84 % avis positifs (36,361 notes)6
 
0.4%
84 % avis positifs (5,654 notes)6
 
0.4%
89 % avis positifs (55,499 notes)6
 
0.4%
83 % avis positifs (247 notes)5
 
0.3%
85 % avis positifs (5,264 notes)5
 
0.3%
Other values (1048)1498
95.2%
2021-06-18T09:00:51.432643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
notes1510
18.4%
1225
14.9%
avis1225
14.9%
positifs1225
14.9%
86141
 
1.7%
85131
 
1.6%
87113
 
1.4%
88112
 
1.4%
84105
 
1.3%
8396
 
1.2%
Other values (971)2331
28.4%

Most occurring characters

ValueCountFrequency (%)
6642
14.7%
s5277
 
11.7%
i3853
 
8.5%
t2819
 
6.2%
o2809
 
6.2%
81663
 
3.7%
e1644
 
3.6%
n1574
 
3.5%
(1572
 
3.5%
)1572
 
3.5%
Other values (92)15861
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23529
52.0%
Decimal Number9229
 
20.4%
Space Separator6642
 
14.7%
Other Punctuation2554
 
5.6%
Open Punctuation1572
 
3.5%
Close Punctuation1572
 
3.5%
Other Letter123
 
0.3%
Uppercase Letter57
 
0.1%
Nonspacing Mark5
 
< 0.1%
Spacing Mark3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
s5277
22.4%
i3853
16.4%
t2819
12.0%
o2809
11.9%
e1644
 
7.0%
n1574
 
6.7%
a1334
 
5.7%
v1266
 
5.4%
p1255
 
5.3%
f1241
 
5.3%
Other values (32)457
 
1.9%
ValueCountFrequency (%)
ي12
 
9.8%
د8
 
6.5%
ف8
 
6.5%
ا8
 
6.5%
ت8
 
6.5%
ر4
 
3.3%
و4
 
3.3%
ع4
 
3.3%
ل4
 
3.3%
إ4
 
3.3%
Other values (25)59
48.0%
ValueCountFrequency (%)
81663
18.0%
11145
12.4%
9923
10.0%
3896
9.7%
2881
9.5%
7794
8.6%
5790
8.6%
6748
8.1%
4719
7.8%
0670
7.3%
ValueCountFrequency (%)
F33
57.9%
P18
31.6%
B4
 
7.0%
O1
 
1.8%
G1
 
1.8%
ValueCountFrequency (%)
%1279
50.1%
,1271
49.8%
:4
 
0.2%
ValueCountFrequency (%)
4
80.0%
1
 
20.0%
ValueCountFrequency (%)
2
66.7%
1
33.3%
ValueCountFrequency (%)
(1572
100.0%
ValueCountFrequency (%)
6642
100.0%
ValueCountFrequency (%)
)1572
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23505
51.9%
Common21569
47.6%
Arabic84
 
0.2%
Cyrillic81
 
0.2%
Thai36
 
0.1%
Khmer11
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
s5277
22.5%
i3853
16.4%
t2819
12.0%
o2809
12.0%
e1644
 
7.0%
n1574
 
6.7%
a1334
 
5.7%
v1266
 
5.4%
p1255
 
5.3%
f1241
 
5.3%
Other values (21)433
 
1.8%
ValueCountFrequency (%)
6642
30.8%
81663
 
7.7%
(1572
 
7.3%
)1572
 
7.3%
%1279
 
5.9%
,1271
 
5.9%
11145
 
5.3%
9923
 
4.3%
3896
 
4.2%
2881
 
4.1%
Other values (6)3725
17.3%
ValueCountFrequency (%)
о12
14.8%
т9
11.1%
л6
 
7.4%
и6
 
7.4%
е6
 
7.4%
н6
 
7.4%
ы6
 
7.4%
в6
 
7.4%
п3
 
3.7%
ж3
 
3.7%
Other values (6)18
22.2%
ValueCountFrequency (%)
ي12
14.3%
د8
 
9.5%
ف8
 
9.5%
ا8
 
9.5%
ت8
 
9.5%
ر4
 
4.8%
و4
 
4.8%
ع4
 
4.8%
ل4
 
4.8%
إ4
 
4.8%
Other values (5)20
23.8%
ValueCountFrequency (%)
4
11.1%
4
11.1%
4
11.1%
4
11.1%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
Other values (4)8
22.2%
ValueCountFrequency (%)
2
18.2%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII45051
99.5%
Arabic84
 
0.2%
Cyrillic81
 
0.2%
Thai36
 
0.1%
None23
 
0.1%
Khmer11
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
6642
14.7%
s5277
 
11.7%
i3853
 
8.6%
t2819
 
6.3%
o2809
 
6.2%
81663
 
3.7%
e1644
 
3.6%
n1574
 
3.5%
(1572
 
3.5%
)1572
 
3.5%
Other values (34)15626
34.7%
ValueCountFrequency (%)
ç9
39.1%
õ9
39.1%
ó5
21.7%
ValueCountFrequency (%)
ي12
14.3%
د8
 
9.5%
ف8
 
9.5%
ا8
 
9.5%
ت8
 
9.5%
ر4
 
4.8%
و4
 
4.8%
ع4
 
4.8%
ل4
 
4.8%
إ4
 
4.8%
Other values (5)20
23.8%
ValueCountFrequency (%)
4
11.1%
4
11.1%
4
11.1%
4
11.1%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
Other values (4)8
22.2%
ValueCountFrequency (%)
2
18.2%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
ValueCountFrequency (%)
о12
14.8%
т9
11.1%
л6
 
7.4%
и6
 
7.4%
е6
 
7.4%
н6
 
7.4%
ы6
 
7.4%
в6
 
7.4%
п3
 
3.7%
ж3
 
3.7%
Other values (6)18
22.2%

merchant_rating_count
Real number (ℝ≥0)

Distinct917
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26495.8328
Minimum0
Maximum2174765
Zeros1
Zeros (%)0.1%
Memory size12.4 KiB
2021-06-18T09:00:51.545939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.4
Q11987
median7936
Q324564
95-th percentile105015
Maximum2174765
Range2174765
Interquartile range (IQR)22577

Descriptive statistics

Standard deviation78474.45561
Coefficient of variation (CV)2.961765957
Kurtosis380.1505712
Mean26495.8328
Median Absolute Deviation (MAD)7334
Skewness15.88901874
Sum41677945
Variance6158240183
MonotocityNot monotonic
2021-06-18T09:00:51.665816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3216815
 
1.0%
1230912
 
0.8%
800939
 
0.6%
429198
 
0.5%
881938
 
0.5%
106008
 
0.5%
317
 
0.4%
554997
 
0.4%
104746
 
0.4%
556706
 
0.4%
Other values (907)1487
94.5%
ValueCountFrequency (%)
01
 
0.1%
32
0.1%
42
0.1%
64
0.3%
81
 
0.1%
ValueCountFrequency (%)
21747651
 
0.1%
8398823
0.2%
4027433
0.2%
3668981
 
0.1%
3304051
 
0.1%

merchant_rating
Real number (ℝ≥0)

Distinct952
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.032345119
Minimum2.333333333
Maximum5
Zeros0
Zeros (%)0.0%
Memory size12.4 KiB
2021-06-18T09:00:51.794801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.333333333
5-th percentile3.70758405
Q13.917353419
median4.040650407
Q34.161796723
95-th percentile4.325714254
Maximum5
Range2.666666667
Interquartile range (IQR)0.244443304

Descriptive statistics

Standard deviation0.204767997
Coefficient of variation (CV)0.05078136691
Kurtosis5.316849955
Mean4.032345119
Median Absolute Deviation (MAD)0.12228306
Skewness-1.029755428
Sum6342.878873
Variance0.04192993259
MonotocityNot monotonic
2021-06-18T09:00:51.930587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.88454364615
 
1.0%
4.04517020112
 
0.8%
4.006692229
 
0.6%
4.1059670548
 
0.5%
4.0808907748
 
0.5%
3.867547178
 
0.5%
4.1388853857
 
0.4%
4.1469573476
 
0.4%
3.9675201456
 
0.4%
3.9587902376
 
0.4%
Other values (942)1488
94.6%
ValueCountFrequency (%)
2.3333333331
0.1%
2.9411764711
0.1%
31
0.1%
3.0344827591
0.1%
3.0389610391
0.1%
ValueCountFrequency (%)
51
0.1%
4.577519381
0.1%
4.5218658891
0.1%
4.51251
0.1%
4.5014720311
0.1%

merchant_id
Categorical

HIGH CARDINALITY

Distinct958
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
558c2cdc89d53c4005ea2920
 
15
5acaf29d5ebcfd72403106a8
 
12
583138b06339b410ab9663ec
 
9
564d8a9ac0f55a1276cd96f8
 
8
582833faea77701b456c786a
 
8
Other values (953)
1521 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters37752
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique635 ?
Unique (%)40.4%

Sample

1st row595097d6a26f6e070cb878d1
2nd row56458aa03a698c35c9050988
3rd row5d464a1ffdf7bc44ee933c65
4th row58cfdefdacb37b556efdff7c
5th row5ab3b592c3911a095ad5dadb
ValueCountFrequency (%)
558c2cdc89d53c4005ea292015
 
1.0%
5acaf29d5ebcfd72403106a812
 
0.8%
583138b06339b410ab9663ec9
 
0.6%
564d8a9ac0f55a1276cd96f88
 
0.5%
582833faea77701b456c786a8
 
0.5%
5533c83986ff95173dc017d08
 
0.5%
5926c5ace8ff5525241b368d7
 
0.4%
56458aa03a698c35c90509886
 
0.4%
5b160017daac45594728d9ba6
 
0.4%
57b03628c676b3573ba2a0816
 
0.4%
Other values (948)1488
94.6%
2021-06-18T09:00:52.206721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
558c2cdc89d53c4005ea292015
 
1.0%
5acaf29d5ebcfd72403106a812
 
0.8%
583138b06339b410ab9663ec9
 
0.6%
564d8a9ac0f55a1276cd96f88
 
0.5%
582833faea77701b456c786a8
 
0.5%
5533c83986ff95173dc017d08
 
0.5%
5926c5ace8ff5525241b368d7
 
0.4%
56458aa03a698c35c90509886
 
0.4%
5b160017daac45594728d9ba6
 
0.4%
57b03628c676b3573ba2a0816
 
0.4%
Other values (948)1488
94.6%

Most occurring characters

ValueCountFrequency (%)
54010
 
10.6%
82449
 
6.5%
62433
 
6.4%
02423
 
6.4%
72364
 
6.3%
12333
 
6.2%
32303
 
6.1%
42255
 
6.0%
a2252
 
6.0%
d2224
 
5.9%
Other values (6)12706
33.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number24879
65.9%
Lowercase Letter12873
34.1%

Most frequent character per category

ValueCountFrequency (%)
54010
16.1%
82449
9.8%
62433
9.8%
02423
9.7%
72364
9.5%
12333
9.4%
32303
9.3%
42255
9.1%
92182
8.8%
22127
8.5%
ValueCountFrequency (%)
a2252
17.5%
d2224
17.3%
c2219
17.2%
e2157
16.8%
b2104
16.3%
f1917
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common24879
65.9%
Latin12873
34.1%

Most frequent character per script

ValueCountFrequency (%)
54010
16.1%
82449
9.8%
62433
9.8%
02423
9.7%
72364
9.5%
12333
9.4%
32303
9.3%
42255
9.1%
92182
8.8%
22127
8.5%
ValueCountFrequency (%)
a2252
17.5%
d2224
17.3%
c2219
17.2%
e2157
16.8%
b2104
16.3%
f1917
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII37752
100.0%

Most frequent character per block

ValueCountFrequency (%)
54010
 
10.6%
82449
 
6.5%
62433
 
6.4%
02423
 
6.4%
72364
 
6.3%
12333
 
6.2%
32303
 
6.1%
42255
 
6.0%
a2252
 
6.0%
d2224
 
5.9%
Other values (6)12706
33.7%

merchant_has_profile_picture
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
0
1347 
1
226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1573
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01347
85.6%
1226
 
14.4%
2021-06-18T09:00:52.418859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-18T09:00:52.485090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
01347
85.6%
1226
 
14.4%

Most occurring characters

ValueCountFrequency (%)
01347
85.6%
1226
 
14.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1573
100.0%

Most frequent character per category

ValueCountFrequency (%)
01347
85.6%
1226
 
14.4%

Most occurring scripts

ValueCountFrequency (%)
Common1573
100.0%

Most frequent character per script

ValueCountFrequency (%)
01347
85.6%
1226
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1573
100.0%

Most frequent character per block

ValueCountFrequency (%)
01347
85.6%
1226
 
14.4%

merchant_profile_picture
Categorical

HIGH CARDINALITY
MISSING

Distinct125
Distinct (%)55.3%
Missing1347
Missing (%)85.6%
Memory size12.4 KiB
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5acaf29d5ebcfd72403106a8.jpg
 
12
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5533c83986ff95173dc017d0.jpg
 
8
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_58ad449708de0c6dc59d9e06.jpg
 
6
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_55c8a4c33a698c6010edcd9e.jpg
 
6
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5268759b736046671957190c.jpg
 
5
Other values (120)
189 

Length

Max length99
Median length99
Mean length99
Min length99

Characters and Unicode

Total characters22374
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)35.0%

Sample

1st rowhttps://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5652f4053a698c76dc9a3f37.jpg
2nd rowhttps://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_55ed5a3362e273427107759e.jpg
3rd rowhttps://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_539937d634067e06707b1a8e.jpg
4th rowhttps://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_55ed5a3362e273427107759e.jpg
5th rowhttps://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_57108cd6a995b507211ef8fb.jpg
ValueCountFrequency (%)
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5acaf29d5ebcfd72403106a8.jpg12
 
0.8%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5533c83986ff95173dc017d0.jpg8
 
0.5%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_58ad449708de0c6dc59d9e06.jpg6
 
0.4%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_55c8a4c33a698c6010edcd9e.jpg6
 
0.4%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5268759b736046671957190c.jpg5
 
0.3%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_577fb2b368116418674befd9.jpg5
 
0.3%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5495976a40b3782bc8b3654a.jpg4
 
0.3%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_54db03867851097093c0efe7.jpg4
 
0.3%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_557ed5b886d66519ff242099.jpg4
 
0.3%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_55c8029641d799421d41fe3a.jpg4
 
0.3%
Other values (115)168
 
10.7%
(Missing)1347
85.6%
2021-06-18T09:00:52.706220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5acaf29d5ebcfd72403106a8.jpg12
 
5.3%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5533c83986ff95173dc017d0.jpg8
 
3.5%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_58ad449708de0c6dc59d9e06.jpg6
 
2.7%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_55c8a4c33a698c6010edcd9e.jpg6
 
2.7%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_577fb2b368116418674befd9.jpg5
 
2.2%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5268759b736046671957190c.jpg5
 
2.2%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_557ed5b886d66519ff242099.jpg4
 
1.8%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_55c8029641d799421d41fe3a.jpg4
 
1.8%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5495976a40b3782bc8b3654a.jpg4
 
1.8%
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_58131ddf6f55296033923a7c.jpg4
 
1.8%
Other values (115)168
74.3%

Most occurring characters

ValueCountFrequency (%)
e1663
 
7.4%
a1413
 
6.3%
s1356
 
6.1%
t1130
 
5.1%
p1130
 
5.1%
-1130
 
5.1%
c965
 
4.3%
/904
 
4.0%
m904
 
4.0%
o904
 
4.0%
Other values (25)10875
48.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15103
67.5%
Decimal Number4107
 
18.4%
Other Punctuation1808
 
8.1%
Dash Punctuation1130
 
5.1%
Connector Punctuation226
 
1.0%

Most frequent character per category

ValueCountFrequency (%)
e1663
 
11.0%
a1413
 
9.4%
s1356
 
9.0%
t1130
 
7.5%
p1130
 
7.5%
c965
 
6.4%
m904
 
6.0%
o904
 
6.0%
d771
 
5.1%
w678
 
4.5%
Other values (10)4189
27.7%
ValueCountFrequency (%)
3575
14.0%
1570
13.9%
5557
13.6%
6395
9.6%
0391
9.5%
8376
9.2%
7358
8.7%
9314
7.6%
4295
7.2%
2276
6.7%
ValueCountFrequency (%)
/904
50.0%
.678
37.5%
:226
 
12.5%
ValueCountFrequency (%)
-1130
100.0%
ValueCountFrequency (%)
_226
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15103
67.5%
Common7271
32.5%

Most frequent character per script

ValueCountFrequency (%)
e1663
 
11.0%
a1413
 
9.4%
s1356
 
9.0%
t1130
 
7.5%
p1130
 
7.5%
c965
 
6.4%
m904
 
6.0%
o904
 
6.0%
d771
 
5.1%
w678
 
4.5%
Other values (10)4189
27.7%
ValueCountFrequency (%)
-1130
15.5%
/904
12.4%
.678
9.3%
3575
7.9%
1570
7.8%
5557
7.7%
6395
 
5.4%
0391
 
5.4%
8376
 
5.2%
7358
 
4.9%
Other values (5)1337
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII22374
100.0%

Most frequent character per block

ValueCountFrequency (%)
e1663
 
7.4%
a1413
 
6.3%
s1356
 
6.1%
t1130
 
5.1%
p1130
 
5.1%
-1130
 
5.1%
c965
 
4.3%
/904
 
4.0%
m904
 
4.0%
o904
 
4.0%
Other values (25)10875
48.6%

product_url
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1341
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
https://www.wish.com/c/5eb4dd169263020a42be1a88
 
3
https://www.wish.com/c/5e142dee04c3e579e89576a3
 
3
https://www.wish.com/c/5eba5b1c29367c77b5c0eb35
 
3
https://www.wish.com/c/5ea91e4d29b81241e1d43b27
 
3
https://www.wish.com/c/5e9a74e447f7d92c8db8d14b
 
3
Other values (1336)
1558 

Length

Max length47
Median length47
Mean length47
Min length47

Characters and Unicode

Total characters73931
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1130 ?
Unique (%)71.8%

Sample

1st rowhttps://www.wish.com/c/5e9ae51d43d6a96e303acdb0
2nd rowhttps://www.wish.com/c/58940d436a0d3d5da4e95a38
3rd rowhttps://www.wish.com/c/5ea10e2c617580260d55310a
4th rowhttps://www.wish.com/c/5cedf17ad1d44c52c59e4aca
5th rowhttps://www.wish.com/c/5ebf5819ebac372b070b0e70
ValueCountFrequency (%)
https://www.wish.com/c/5eb4dd169263020a42be1a883
 
0.2%
https://www.wish.com/c/5e142dee04c3e579e89576a33
 
0.2%
https://www.wish.com/c/5eba5b1c29367c77b5c0eb353
 
0.2%
https://www.wish.com/c/5ea91e4d29b81241e1d43b273
 
0.2%
https://www.wish.com/c/5e9a74e447f7d92c8db8d14b3
 
0.2%
https://www.wish.com/c/5eba05b08c884a0bddd0ad963
 
0.2%
https://www.wish.com/c/5c80e8a150c63d28c67b8f143
 
0.2%
https://www.wish.com/c/5e9dad8cbc19c300417e17333
 
0.2%
https://www.wish.com/c/5e93d60ebc5446aedde50c503
 
0.2%
https://www.wish.com/c/5cde56ea6bbbd86b1cbab4a83
 
0.2%
Other values (1331)1543
98.1%
2021-06-18T09:00:52.958402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.wish.com/c/5eb4dd169263020a42be1a883
 
0.2%
https://www.wish.com/c/5e142dee04c3e579e89576a33
 
0.2%
https://www.wish.com/c/5eba5b1c29367c77b5c0eb353
 
0.2%
https://www.wish.com/c/5ea91e4d29b81241e1d43b273
 
0.2%
https://www.wish.com/c/5e9a74e447f7d92c8db8d14b3
 
0.2%
https://www.wish.com/c/5eba05b08c884a0bddd0ad963
 
0.2%
https://www.wish.com/c/5c80e8a150c63d28c67b8f143
 
0.2%
https://www.wish.com/c/5e9dad8cbc19c300417e17333
 
0.2%
https://www.wish.com/c/5e93d60ebc5446aedde50c503
 
0.2%
https://www.wish.com/c/5cde56ea6bbbd86b1cbab4a83
 
0.2%
Other values (1331)1543
98.1%

Most occurring characters

ValueCountFrequency (%)
/6292
 
8.5%
w6292
 
8.5%
c5604
 
7.6%
53676
 
5.0%
h3146
 
4.3%
t3146
 
4.3%
s3146
 
4.3%
.3146
 
4.3%
e2791
 
3.8%
02436
 
3.3%
Other values (17)34256
46.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter39185
53.0%
Decimal Number23735
32.1%
Other Punctuation11011
 
14.9%

Most frequent character per category

ValueCountFrequency (%)
w6292
16.1%
c5604
14.3%
h3146
8.0%
t3146
8.0%
s3146
8.0%
e2791
7.1%
d2346
 
6.0%
b2331
 
5.9%
a2116
 
5.4%
f1975
 
5.0%
Other values (4)6292
16.1%
ValueCountFrequency (%)
53676
15.5%
02436
10.3%
12299
9.7%
22269
9.6%
42242
9.4%
32179
9.2%
92176
9.2%
72164
9.1%
62155
9.1%
82139
9.0%
ValueCountFrequency (%)
/6292
57.1%
.3146
28.6%
:1573
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin39185
53.0%
Common34746
47.0%

Most frequent character per script

ValueCountFrequency (%)
w6292
16.1%
c5604
14.3%
h3146
8.0%
t3146
8.0%
s3146
8.0%
e2791
7.1%
d2346
 
6.0%
b2331
 
5.9%
a2116
 
5.4%
f1975
 
5.0%
Other values (4)6292
16.1%
ValueCountFrequency (%)
/6292
18.1%
53676
10.6%
.3146
9.1%
02436
 
7.0%
12299
 
6.6%
22269
 
6.5%
42242
 
6.5%
32179
 
6.3%
92176
 
6.3%
72164
 
6.2%
Other values (3)5867
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII73931
100.0%

Most frequent character per block

ValueCountFrequency (%)
/6292
 
8.5%
w6292
 
8.5%
c5604
 
7.6%
53676
 
5.0%
h3146
 
4.3%
t3146
 
4.3%
s3146
 
4.3%
.3146
 
4.3%
e2791
 
3.8%
02436
 
3.3%
Other values (17)34256
46.3%

product_picture
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1341
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
https://contestimg.wish.com/api/webimage/5eba05b08c884a0bddd0ad96-medium.jpg
 
3
https://contestimg.wish.com/api/webimage/5eba5b1c29367c77b5c0eb35-medium.jpg
 
3
https://contestimg.wish.com/api/webimage/5ec1e63f7abee20ab93c68f2-medium.jpg
 
3
https://contestimg.wish.com/api/webimage/5dea1d9cec016f062ce8aab1-medium.jpg
 
3
https://contestimg.wish.com/api/webimage/5ebe0ead593b960eb1c82d0b-medium.jpg
 
3
Other values (1336)
1558 

Length

Max length76
Median length76
Mean length76
Min length76

Characters and Unicode

Total characters119548
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1130 ?
Unique (%)71.8%

Sample

1st rowhttps://contestimg.wish.com/api/webimage/5e9ae51d43d6a96e303acdb0-medium.jpg
2nd rowhttps://contestimg.wish.com/api/webimage/58940d436a0d3d5da4e95a38-medium.jpg
3rd rowhttps://contestimg.wish.com/api/webimage/5ea10e2c617580260d55310a-medium.jpg
4th rowhttps://contestimg.wish.com/api/webimage/5cedf17ad1d44c52c59e4aca-medium.jpg
5th rowhttps://contestimg.wish.com/api/webimage/5ebf5819ebac372b070b0e70-medium.jpg
ValueCountFrequency (%)
https://contestimg.wish.com/api/webimage/5eba05b08c884a0bddd0ad96-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5eba5b1c29367c77b5c0eb35-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5ec1e63f7abee20ab93c68f2-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5dea1d9cec016f062ce8aab1-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5ebe0ead593b960eb1c82d0b-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5ee8875404718a4bba2d6348-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5eaa6d9c8d99eb3ec06709f4-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5eb4dd169263020a42be1a88-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5ebff6d34a4cf4438dba5d80-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5e9a74e447f7d92c8db8d14b-medium.jpg3
 
0.2%
Other values (1331)1543
98.1%
2021-06-18T09:00:53.213409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://contestimg.wish.com/api/webimage/5eba05b08c884a0bddd0ad96-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5eba5b1c29367c77b5c0eb35-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5ec1e63f7abee20ab93c68f2-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5dea1d9cec016f062ce8aab1-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5ebe0ead593b960eb1c82d0b-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5ee8875404718a4bba2d6348-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5eaa6d9c8d99eb3ec06709f4-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5eb4dd169263020a42be1a88-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5ebff6d34a4cf4438dba5d80-medium.jpg3
 
0.2%
https://contestimg.wish.com/api/webimage/5e9a74e447f7d92c8db8d14b-medium.jpg3
 
0.2%
Other values (1331)1543
98.1%

Most occurring characters

ValueCountFrequency (%)
e9083
 
7.6%
/7865
 
6.6%
i7865
 
6.6%
m7865
 
6.6%
t6292
 
5.3%
c5604
 
4.7%
a5262
 
4.4%
p4719
 
3.9%
s4719
 
3.9%
g4719
 
3.9%
Other values (22)55555
46.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter80083
67.0%
Decimal Number23735
 
19.9%
Other Punctuation14157
 
11.8%
Dash Punctuation1573
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e9083
11.3%
i7865
 
9.8%
m7865
 
9.8%
t6292
 
7.9%
c5604
 
7.0%
a5262
 
6.6%
p4719
 
5.9%
s4719
 
5.9%
g4719
 
5.9%
d3919
 
4.9%
Other values (8)20036
25.0%
ValueCountFrequency (%)
53676
15.5%
02436
10.3%
12299
9.7%
22269
9.6%
42242
9.4%
32179
9.2%
92176
9.2%
72164
9.1%
62155
9.1%
82139
9.0%
ValueCountFrequency (%)
/7865
55.6%
.4719
33.3%
:1573
 
11.1%
ValueCountFrequency (%)
-1573
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin80083
67.0%
Common39465
33.0%

Most frequent character per script

ValueCountFrequency (%)
e9083
11.3%
i7865
 
9.8%
m7865
 
9.8%
t6292
 
7.9%
c5604
 
7.0%
a5262
 
6.6%
p4719
 
5.9%
s4719
 
5.9%
g4719
 
5.9%
d3919
 
4.9%
Other values (8)20036
25.0%
ValueCountFrequency (%)
/7865
19.9%
.4719
12.0%
53676
9.3%
02436
 
6.2%
12299
 
5.8%
22269
 
5.7%
42242
 
5.7%
32179
 
5.5%
92176
 
5.5%
72164
 
5.5%
Other values (4)7440
18.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII119548
100.0%

Most frequent character per block

ValueCountFrequency (%)
e9083
 
7.6%
/7865
 
6.6%
i7865
 
6.6%
m7865
 
6.6%
t6292
 
5.3%
c5604
 
4.7%
a5262
 
4.4%
p4719
 
3.9%
s4719
 
3.9%
g4719
 
3.9%
Other values (22)55555
46.5%

product_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1341
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
5eba05b08c884a0bddd0ad96
 
3
5ee8875404718a4bba2d6348
 
3
5e93d60ebc5446aedde50c50
 
3
5ebff6d34a4cf4438dba5d80
 
3
5e16cb87e6dd7c03be24b28a
 
3
Other values (1336)
1558 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters37752
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1130 ?
Unique (%)71.8%

Sample

1st row5e9ae51d43d6a96e303acdb0
2nd row58940d436a0d3d5da4e95a38
3rd row5ea10e2c617580260d55310a
4th row5cedf17ad1d44c52c59e4aca
5th row5ebf5819ebac372b070b0e70
ValueCountFrequency (%)
5eba05b08c884a0bddd0ad963
 
0.2%
5ee8875404718a4bba2d63483
 
0.2%
5e93d60ebc5446aedde50c503
 
0.2%
5ebff6d34a4cf4438dba5d803
 
0.2%
5e16cb87e6dd7c03be24b28a3
 
0.2%
5e9dad8cbc19c300417e17333
 
0.2%
5e9a74e447f7d92c8db8d14b3
 
0.2%
5eb2200b989caa081980b8123
 
0.2%
5ec1e63f7abee20ab93c68f23
 
0.2%
5ea91e4d29b81241e1d43b273
 
0.2%
Other values (1331)1543
98.1%
2021-06-18T09:00:53.484195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5eba05b08c884a0bddd0ad963
 
0.2%
5ee8875404718a4bba2d63483
 
0.2%
5e93d60ebc5446aedde50c503
 
0.2%
5ebff6d34a4cf4438dba5d803
 
0.2%
5e16cb87e6dd7c03be24b28a3
 
0.2%
5e9dad8cbc19c300417e17333
 
0.2%
5e9a74e447f7d92c8db8d14b3
 
0.2%
5eb2200b989caa081980b8123
 
0.2%
5ec1e63f7abee20ab93c68f23
 
0.2%
5ea91e4d29b81241e1d43b273
 
0.2%
Other values (1331)1543
98.1%

Most occurring characters

ValueCountFrequency (%)
53676
 
9.7%
e2791
 
7.4%
c2458
 
6.5%
02436
 
6.5%
d2346
 
6.2%
b2331
 
6.2%
12299
 
6.1%
22269
 
6.0%
42242
 
5.9%
32179
 
5.8%
Other values (6)12725
33.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number23735
62.9%
Lowercase Letter14017
37.1%

Most frequent character per category

ValueCountFrequency (%)
53676
15.5%
02436
10.3%
12299
9.7%
22269
9.6%
42242
9.4%
32179
9.2%
92176
9.2%
72164
9.1%
62155
9.1%
82139
9.0%
ValueCountFrequency (%)
e2791
19.9%
c2458
17.5%
d2346
16.7%
b2331
16.6%
a2116
15.1%
f1975
14.1%

Most occurring scripts

ValueCountFrequency (%)
Common23735
62.9%
Latin14017
37.1%

Most frequent character per script

ValueCountFrequency (%)
53676
15.5%
02436
10.3%
12299
9.7%
22269
9.6%
42242
9.4%
32179
9.2%
92176
9.2%
72164
9.1%
62155
9.1%
82139
9.0%
ValueCountFrequency (%)
e2791
19.9%
c2458
17.5%
d2346
16.7%
b2331
16.6%
a2116
15.1%
f1975
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII37752
100.0%

Most frequent character per block

ValueCountFrequency (%)
53676
 
9.7%
e2791
 
7.4%
c2458
 
6.5%
02436
 
6.5%
d2346
 
6.2%
b2331
 
6.2%
12299
 
6.1%
22269
 
6.0%
42242
 
5.9%
32179
 
5.8%
Other values (6)12725
33.7%

theme
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
summer
1573 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters9438
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsummer
2nd rowsummer
3rd rowsummer
4th rowsummer
5th rowsummer
ValueCountFrequency (%)
summer1573
100.0%
2021-06-18T09:00:54.005658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-18T09:00:54.070408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
summer1573
100.0%

Most occurring characters

ValueCountFrequency (%)
m3146
33.3%
s1573
16.7%
u1573
16.7%
e1573
16.7%
r1573
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9438
100.0%

Most frequent character per category

ValueCountFrequency (%)
m3146
33.3%
s1573
16.7%
u1573
16.7%
e1573
16.7%
r1573
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin9438
100.0%

Most frequent character per script

ValueCountFrequency (%)
m3146
33.3%
s1573
16.7%
u1573
16.7%
e1573
16.7%
r1573
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII9438
100.0%

Most frequent character per block

ValueCountFrequency (%)
m3146
33.3%
s1573
16.7%
u1573
16.7%
e1573
16.7%
r1573
16.7%

crawl_month
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
2020-08
1573 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters11011
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-08
2nd row2020-08
3rd row2020-08
4th row2020-08
5th row2020-08
ValueCountFrequency (%)
2020-081573
100.0%
2021-06-18T09:00:54.221668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-18T09:00:54.288195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2020-081573
100.0%

Most occurring characters

ValueCountFrequency (%)
04719
42.9%
23146
28.6%
-1573
 
14.3%
81573
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9438
85.7%
Dash Punctuation1573
 
14.3%

Most frequent character per category

ValueCountFrequency (%)
04719
50.0%
23146
33.3%
81573
 
16.7%
ValueCountFrequency (%)
-1573
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11011
100.0%

Most frequent character per script

ValueCountFrequency (%)
04719
42.9%
23146
28.6%
-1573
 
14.3%
81573
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11011
100.0%

Most frequent character per block

ValueCountFrequency (%)
04719
42.9%
23146
28.6%
-1573
 
14.3%
81573
 
14.3%

Interactions

2021-06-18T09:00:01.724663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:01.850815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:01.972598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:02.093140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:02.204774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:02.308826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:02.438595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:02.563514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:02.706218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:02.827907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:02.944105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:03.055458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:03.177653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:03.289718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:03.399663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:03.523309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:03.655000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:03.777315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:03.915962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:04.037674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:04.199282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:04.350299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:04.491415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:04.634309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:04.766208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:04.927839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:05.098884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:05.289491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:05.445076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:05.903872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:06.166545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:06.384960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:06.622327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:06.841739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:07.040592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:07.241056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:07.451493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:07.644976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:07.795588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:07.963432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:08.119016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:08.301529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:08.455147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:08.571811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:08.704452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:08.867018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:09.033880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:09.218401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:09.386966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:09.534543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:09.667188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:09.796841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:09.934502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:10.074414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:10.207051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:10.342659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:10.468352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:10.611968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:10.740625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:10.912168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:11.078002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:11.222655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:11.364275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:11.489679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:11.719201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:11.854806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:11.977509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:12.113508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:12.252107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:12.399713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:12.543336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:12.680963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:12.806625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:12.928329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:13.046419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:13.157118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:13.277767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:13.397447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:13.522134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:13.658778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:13.786418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:13.932035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:14.116910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:14.268539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:14.425086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:14.550782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:14.676443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:14.844964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:15.009553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:15.164443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:15.369895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:15.549414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:15.732924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:15.932391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:16.116334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:16.262941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:16.396607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:16.540232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:16.702797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:16.847409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:16.987005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:17.145882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:17.349339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:17.486970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-18T09:00:17.648540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-18T09:00:54.693426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-18T09:00:55.028552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-18T09:00:55.360951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-18T09:00:55.676738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-18T09:00:41.166881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-06-18T09:00:41.467294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

title_in_frenchtitle_translatedlisted_priceretail_pricecurrency_buyerunits_solduses_ad_boostsratingrating_countrating_five_countrating_four_countrating_three_countrating_two_countrating_one_countbadges_countbadge_local_productbadge_product_qualitybadge_fast_shippingtagsproduct_colorproduct_variation_size_idproduct_variation_inventoryshipping_option_nameshipping_option_priceshipping_is_expresscountries_shipped_toinventory_totalhas_urgency_bannerurgency_textorigin_countrymerchant_titlemerchant_namemerchant_info_subtitlemerchant_rating_countmerchant_ratingmerchant_idmerchant_has_profile_picturemerchant_profile_pictureproduct_urlproduct_pictureproduct_idthemecrawl_month
02020 Summer Vintage Flamingo Print Pajamas Set Casual Loose T Shirt Top And Elastic Shorts Women Sleepwear Night Wear Loungewear Sets2020 Summer Vintage Flamingo Print Pajamas Set Casual Loose T Shirt Top And Elastic Shorts Women Sleepwear Night Wear Loungewear Sets16.0014EUR10003.765426.08.010.01.09.00000Summer,Fashion,womenunderwearsuit,printedpajamasset,womencasualshort,Women's Fashion,flamingo,loungewearset,Casual,Shirt,casualsleepwear,Shorts,flamingotshirt,Elastic,Vintage,Tops,tshirtandshortsset,Women,Sleepwear,Print,womenpajamasset,womennightwear,Pajamas,womensleepwearsetwhiteM50Livraison standard4034501.0Quantité limitée !CNzgrdejiazgrdejia(568 notes)5684.128521595097d6a26f6e070cb878d10NaNhttps://www.wish.com/c/5e9ae51d43d6a96e303acdb0https://contestimg.wish.com/api/webimage/5e9ae51d43d6a96e303acdb0-medium.jpg5e9ae51d43d6a96e303acdb0summer2020-08
1SSHOUSE Summer Casual Sleeveless Soirée Party Soirée sans manches Vêtements de plage sexy Mini robe femme wshC1612242400387A21Women's Casual Summer Sleeveless Sexy Mini Dress8.0022EUR2000013.4561352269.01027.01118.0644.01077.00000Mini,womens dresses,Summer,Patchwork,fashion dress,Dress,Mini dress,Women's Fashion,Women S Clothing,backless,party,summer dresses,sleeveless,sexy,CasualgreenXS50Livraison standard2041501.0Quantité limitée !CNSaraHousesarahouse83 % avis positifs (17,752 notes)177523.89967356458aa03a698c35c90509880NaNhttps://www.wish.com/c/58940d436a0d3d5da4e95a38https://contestimg.wish.com/api/webimage/58940d436a0d3d5da4e95a38-medium.jpg58940d436a0d3d5da4e95a38summer2020-08
22020 Nouvelle Arrivée Femmes Printemps et Été Plage Porter Longue Mince Cardigan Ouvert Avant Kimono Vert Feuille Imprimé En Mousseline de Soie Cardigan S-5XL2020 New Arrival Women Spring and Summer Beach Wear Long Thin Cardigan Open Front Kimono Green Leaf Printed Chiffon Cardigan S-5XL8.0043EUR10003.57145.04.02.00.03.00000Summer,cardigan,women beachwear,chiffon,Sexy women,Coat,summercardigan,openfront,short sleeves,Swimsuit,Women's Fashion,leaf,Green,printed,Spring,longcardigan,Women,Beach,kimonoleopardprintXS1Livraison standard3036501.0Quantité limitée !CNhxt520hxt52086 % avis positifs (295 notes)2953.9898315d464a1ffdf7bc44ee933c650NaNhttps://www.wish.com/c/5ea10e2c617580260d55310ahttps://contestimg.wish.com/api/webimage/5ea10e2c617580260d55310a-medium.jpg5ea10e2c617580260d55310asummer2020-08
3Hot Summer Cool T-shirt pour les femmes Mode Tops Abeille Lettres imprimées Manches courtes O Neck Coton T-shirts Tops Tee VêtementsHot Summer Cool T Shirt for Women Fashion Tops Bee Printed Letters Short Sleeve O Neck Cotton T-shirts Tops Tee Clothing8.008EUR500014.03579295.0119.087.042.036.00000Summer,Shorts,Cotton,Cotton T Shirt,Sleeve,printedletterstop,Clothing,Tops,Necks,short sleeves,Women's Fashion,Women Clothing,printed,Women,tshirtforwomen,Fashion,T Shirts,ShirtblackM50Livraison standard204150NaNNaNCNallenfanallenfan(23,832 notes)238324.02043558cfdefdacb37b556efdff7c0NaNhttps://www.wish.com/c/5cedf17ad1d44c52c59e4acahttps://contestimg.wish.com/api/webimage/5cedf17ad1d44c52c59e4aca-medium.jpg5cedf17ad1d44c52c59e4acasummer2020-08
4Femmes Shorts d'été à lacets taille élastique lâche mince pantalon décontracté, plus la taille S-8XLWomen Summer Shorts Lace Up Elastic Waistband Loose Thin Casual Pants Plus Size S-8XL2.723EUR10013.10206.04.02.02.06.00000Summer,Plus Size,Lace,Casual pants,Bottom,pants,Loose,Women's Fashion,Shorts,Lace Up,Elastic,Casual,WomenyellowS1Livraison standard1035501.0Quantité limitée !CNyoungpeopleshophappyhorses85 % avis positifs (14,482 notes)144824.0015885ab3b592c3911a095ad5dadb0NaNhttps://www.wish.com/c/5ebf5819ebac372b070b0e70https://contestimg.wish.com/api/webimage/5ebf5819ebac372b070b0e70-medium.jpg5ebf5819ebac372b070b0e70summer2020-08
5Plus la taille d'été femmes décontracté sans manches barboteuses combinaisons combinaison de couleur unie jarretelles pantalons lâche salopettePlus Size Summer Women Casual Sleeveless Rompers Jumpsuits Solid Color Suspender Ttrousers Loose Overalls3.929EUR1005.0011.00.00.00.00.00000Deep V-Neck,Summer,Plus Size,Spaghetti Strap,Overalls,Women's Fashion,sleeveless,Women,Casual,jumpsuitnavyblueSize-XS1Livraison standard104050NaNNaNCNzhoulinglingazhoulinglinga75 % avis positifs (65 notes)653.5076925e4b9c3801ba9d210036fc5a0NaNhttps://www.wish.com/c/5ec645bafd107a02279c8c54https://contestimg.wish.com/api/webimage/5ec645bafd107a02279c8c54-medium.jpg5ec645bafd107a02279c8c54summer2020-08
6Women Fashion Loose Lace Blouse Blouse V Neck Bat Sleeves T Shirt Hollow Out Tops Plus Grande Taille XS-8XLWomen Fashion Loose Lace Blouse V Neck Bat Sleeves T Shirt Hollow Out Tops Plus Size XS-8XL7.006EUR5000003.8467423172.01352.0971.0490.0757.00000blouse,Women,lace t shirt,summer t-shirts,Lace,Sleeve,Women Blouse,loose shirt,Short Sleeve Blouses,Pure Color,Womens Blouse,Bat,lace shirts,Necks,Women's Fashion,Plus Size,loose t-shirt,Short Sleeve T-Shirt,Fashion,Tops,ShirtwhiteXS50Livraison standard203150NaNNaNCNUnique Li Fashion Shopuniquelifashionshopbb657bfe91d211e598c7063a14dc88b586 % avis positifs (10,194 notes)101944.0765165652f4053a698c76dc9a3f371https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5652f4053a698c76dc9a3f37.jpghttps://www.wish.com/c/5c63a337d5e2ce4bbb3152cfhttps://contestimg.wish.com/api/webimage/5c63a337d5e2ce4bbb3152cf-medium.jpg5c63a337d5e2ce4bbb3152cfsummer2020-08
7Robe tunique ample femme Robe d'été Robe en jean Robe chemise en jean Robe droiteWomen's Baggy Tunic Dress Summer Dress Denim Dress Denim Shirt Dress Shift Dress12.0011EUR100003.76286120.056.061.018.031.00000Jeans,Fashion,tunic,Shirt,Summer,Dress,Denim,summer dress,denimjeansdres,short sleeves,casual dresses,Women's Fashion,Tunic dress,minishirtdres,Lines,mididreblueM.50Livraison standard3013950NaNNaNCNSo Bandsoband(342 notes)3423.6812875d45349676befe65691dcfbb0NaNhttps://www.wish.com/c/5e0ae5ebc2efb76ccf0a3391https://contestimg.wish.com/api/webimage/5e0ae5ebc2efb76ccf0a3391-medium.jpg5e0ae5ebc2efb76ccf0a3391summer2020-08
8Robe d'été décontractée à manches courtes pour femmesWomen's Summer Casual Dress Fashion Short Sleeve Slim Dress11.0084EUR10013.47156.02.03.01.03.00000slim dress,summer dress,womenshortsleevedre,Sleeve,Summer,Dress,slim,short sleeves,Women's Fashion,Shorts,boho dress,slimfitdre,Fashion,CasualblackM50Livraison standard2036501.0Quantité limitée !CNchenxiangjunjunchenxiangjunjun82 % avis positifs (330 notes)3303.8030305d42980e8388970d32294ddc0NaNhttps://www.wish.com/c/5e6f1fb7fe4a5bb4b8bf36e5https://contestimg.wish.com/api/webimage/5e6f1fb7fe4a5bb4b8bf36e5-medium.jpg5e6f1fb7fe4a5bb4b8bf36e5summer2020-08
9Femmes d'été, plus la taille décontractée lâche col en V à manches courtes imprimé floral Blouse TopsSummer Women Plus Size Casual Loose V Neck Short Sleeve Floral Printed Blouse Tops5.7822EUR500003.60687287.0128.092.068.0112.00000blouse,Summer,Plus Size,Floral print,Necks,Sleeve,summer shirt,Loose,short sleeves,Casual,T Shirts,Shorts,Fashion,Floral,Women,Women's Fashion,Tops,printedbeigeS50Livraison standard203350NaNNaNCNLuowei clotheluoweiclothe85 % avis positifs (5,534 notes)55343.9998195ba2251b4315d12ebce873fa0NaNhttps://www.wish.com/c/5ccfaf238a8d535cec2dfb47https://contestimg.wish.com/api/webimage/5ccfaf238a8d535cec2dfb47-medium.jpg5ccfaf238a8d535cec2dfb47summer2020-08

Last rows

title_in_frenchtitle_translatedlisted_priceretail_pricecurrency_buyerunits_solduses_ad_boostsratingrating_countrating_five_countrating_four_countrating_three_countrating_two_countrating_one_countbadges_countbadge_local_productbadge_product_qualitybadge_fast_shippingtagsproduct_colorproduct_variation_size_idproduct_variation_inventoryshipping_option_nameshipping_option_priceshipping_is_expresscountries_shipped_toinventory_totalhas_urgency_bannerurgency_textorigin_countrymerchant_titlemerchant_namemerchant_info_subtitlemerchant_rating_countmerchant_ratingmerchant_idmerchant_has_profile_picturemerchant_profile_pictureproduct_urlproduct_pictureproduct_idthemecrawl_month
1563ZANZEA Femmes Été Polka Dot Kaftan Beach Club Party Longue Maxi Dress HOT Long DressZANZEA Women Summer Polka Dot Kaftan Beach Club Party Long Maxi Dress HOT Long Dress15.0092EUR10003.517426.015.015.07.011.00000Summer,loosedresse,kaftan,long dress,baggydres,Dress,Polkas,robesforwomen,Women's Fashion,34sleevedres,party,roundneckdres,vestido,maxi dress,Beach,polka dot,WomenredL50Envio Padrão403850NaNNaNCNfashionforgirlsguangzhouchanny88% Feedback positivo (151,914 classificações)1519144.12792153aa664438d3046ee44a50240NaNhttps://www.wish.com/c/5da04c1f5949a226113006f1https://contestimg.wish.com/api/webimage/5da04c1f5949a226113006f1-medium.jpg5da04c1f5949a226113006f1summer2020-08
15642018 Femme mode d'été en dentelle Patchwork Patchwork Débardeurs Débardeurs Casual Sleeveless Tops Gilet Chemisier (S-5XL) Grande taille2018 Women fashion Summer Lace Patchwork Tank Tops Casual Sleeveless Tops Vest Blouse (S-5XL) Plus Size5.915EUR100003.38414156.058.072.045.083.00000blouse,Summer,Vest,Fashion,Women Blouse,sleevelessblouse,summer shirt,Loose,tank top,Casual,summerblouse,Women's Fashion,sleeveless tops,women top,casualblouse,WomenWhiteS50Livraison standard2036501.0Quantité limitée !CNliminnyliminny81 % avis positifs (12,134 notes)121343.86690358aec90823ef726994a323fe0NaNhttps://www.wish.com/c/5b4ed29514f0765a8a844592https://contestimg.wish.com/api/webimage/5b4ed29514f0765a8a844592-medium.jpg5b4ed29514f0765a8a844592summer2020-08
1565Nouveau Pantalon De Mode D'été Femmes Leggings Pantalon Déchiré Pantalon Mince Armée Vert Collants PantalonNew Summer Fashion Trousers Women Leggings Ripped Pants Slim Pants Army Green Tights Pants3.008EUR10013.795725.010.013.03.06.00000Summer,Leggings,Fashion,high waist,pants,slim,Women's Fashion,trousers,Green,Army,WomenskyblueXS1Livraison standard1041501.0Quantité limitée !CNbujizhanbujizhan(4,080 notes)40803.987990584a7a381591451e4e3af3df0NaNhttps://www.wish.com/c/5e8f0165e815903d022a3c7chttps://contestimg.wish.com/api/webimage/5e8f0165e815903d022a3c7c-medium.jpg5e8f0165e815903d022a3c7csummer2020-08
1566Robe mi-longue d'été à manches courtes pour femmes Baggy Robes pour femmes Shift Kaftan S-5XLWomens Short Sleeve Baggy Summer Beach Midi Dress Ladies Shift Kaftan Dresses S-5XL11.00134EUR10013.54287.011.04.02.04.00000Summer,Shift Dress,Sleeve,shirt dress,long dress,Beach,Dress,short sleeves,beach dress,Shorts,Midi Dress,Ladies,Women's Fashion,loose dress,kaftandreblackS50Livraison standard304650NaNNaNCNSCOMELYscomely86 % avis positifs (1,926 notes)19264.071651593402ae25c4f54ed4e0abdf0NaNhttps://www.wish.com/c/5d1060d39ed281190dfcec91https://contestimg.wish.com/api/webimage/5d1060d39ed281190dfcec91-medium.jpg5d1060d39ed281190dfcec91summer2020-08
1567Combinaison sans manches pour femmes couleur unie Dames Slim Short Bodycon Rompers Femmes BodySleeveless Solid Color Women Jumpsuit Ladies Slim Short Bodycon Rompers Women Bodysuit8.007EUR2000014.2531271919.0580.0304.0128.0196.01010bodycon jumpsuits,nightwear,Shorts,slim,Body Suit,shortjumpsuit,Women,vestido,Ladies,sleeveless,sexy,Rompers,Casual,jumpsuitblackM50Livraison standard204450NaNNaNCNRell Mailrellmail88 % avis positifs (16,803 notes)168034.15503256455b13b15aab129db58cb70NaNhttps://www.wish.com/c/5c91a7ae7cfe8e4e64c36d97https://contestimg.wish.com/api/webimage/5c91a7ae7cfe8e4e64c36d97-medium.jpg5c91a7ae7cfe8e4e64c36d97summer2020-08
1568Nouvelle Mode Femmes Bohême Pissenlit Imprimer Tee Shirt Lady Fille T-shirt À Manches Courtes Boho Graphique Tee Casual Yoga Top Plus La TailleNew Fashion Women Bohemia Dandelion Print Tee Shirt Lady Girl Short Sleeve T-shirt Boho Graphic Tee Casual Yoga Top Plus Size6.009EUR1000014.081367722.0293.0185.077.090.00000bohemia,Plus Size,dandelionfloralprinted,short sleeves,yoga top,bohotshirt,Cool T-Shirts,Women's Fashion,Fashion,short sleeve shirt,Casual,Women,Shorts,Yoga,Shirt,Sleeve,graphic tee,Tee Shirt,T Shirts,boho,bohoshirt,Print,Casual Tops,TopsnavyblueS50Livraison standard204150NaNNaNCNcxuelin99126cxuelin9912690 % avis positifs (5,316 notes)53164.2246055b507899ab577736508a07820NaNhttps://www.wish.com/c/5d5fadc99febd9356cbc52eehttps://contestimg.wish.com/api/webimage/5d5fadc99febd9356cbc52ee-medium.jpg5d5fadc99febd9356cbc52eesummer2020-08
156910 couleurs femmes shorts d'été lacent ceinture élastique culotte lâche, plus la taille S-6XL10 Color Women Summer Shorts Lace Up Elastic Waistband Loose Panties Plus Size S-6XL2.0056EUR10013.072811.03.01.03.010.00000Summer,Panties,Elastic,Lace,Casual pants,casualshort,summer shorts,Plus Size,Short pants,women shorts,Shorts,Beach Shorts Women,Beach Shorts,loosepant,high waisted shorts,Lace Up,Women's Fashion,WomenlightblueS2Livraison standard1026501.0Quantité limitée !CNsell best quality goodssellbestqualitygoods(4,435 notes)44353.69605454d83b6b6b8a771e478558de0NaNhttps://www.wish.com/c/5eccd22b4497b86fd48f16b4https://contestimg.wish.com/api/webimage/5eccd22b4497b86fd48f16b4-medium.jpg5eccd22b4497b86fd48f16b4summer2020-08
1570Nouveautés Hommes Siwmwear Beach-Shorts Hommes Summer Short de bain court à séchage rapide Beach-Wear SportsNew Men Siwmwear Beach-Shorts Men Summer Quick-Dry Short Swim-Shorts Beach-Wear Sports5.0019EUR10003.715924.015.08.03.09.00000runningshort,Beach Shorts,beachpant,menbeachshort,Men,sailboatshort,beach swimwear,Men's Fashion,Shorts,Summer,men's shorts,SportwhiteSIZE S15Livraison standard201150NaNNaNCNshixueyingshixueying86 % avis positifs (210 notes)2103.9619055b42da1bf64320209fc8da690NaNhttps://www.wish.com/c/5e74be96034d613d42b52dfehttps://contestimg.wish.com/api/webimage/5e74be96034d613d42b52dfe-medium.jpg5e74be96034d613d42b52dfesummer2020-08
1571Mode femmes d'été sans manches robes col en V dos nu robe en dentelle dames robes de plage robe blancheFashion Women Summer Sleeveless Dresses V Neck Backless Lace Dress Ladies Beach Dresses White Dress13.0011EUR10002.5020.01.00.00.01.00000Summer,fashion women,Fashion,Lace,Dresses,Dress,Lace Dress,Women's Fashion,ladies dress,beach dress,Sleeveless dress,backless,women's dress,sleeveless,Ladies,women dress,V-neck Dresses,Women,Beach,white,NeckswhiteSize S.36Livraison standard302950NaNNaNCNmodaimodai77 % avis positifs (31 notes)313.7741945d56b32c40defd78043d5af90NaNhttps://www.wish.com/c/5eda07ab0e295c2097c36590https://contestimg.wish.com/api/webimage/5eda07ab0e295c2097c36590-medium.jpg5eda07ab0e295c2097c36590summer2020-08
1572Pantalon de yoga pour femmes à la mode Slim Fit Fitness Running LeggingsFashion Women Yoga Pants Slim Fit Fitness Running Leggings7.006EUR10014.07148.03.01.00.02.00000Summer,Leggings,slim,Yoga,pants,Slim Fit,Women's Fashion,Running,Fashion,Sport,Fitness,WomenredS50Livraison standard204150NaNNaNCNAISHOPPINGMALLaishoppingmall90 % avis positifs (7,023 notes)70234.2359395a409cf87b584e7951b2e25f0NaNhttps://www.wish.com/c/5e857321f53c3d2d8f25e7edhttps://contestimg.wish.com/api/webimage/5e857321f53c3d2d8f25e7ed-medium.jpg5e857321f53c3d2d8f25e7edsummer2020-08